Association of diet during pregnancy with adverse pregnancy outcomes: a cross-sectional study of pregnant women 20-44 years of age

Association of diet during pregnancy with adverse pregnancy outcomes: a cross-sectional study of pregnant women 20-44 years of age | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of diet during pregnancy with adverse pregnancy outcomes: a cross-sectional study of pregnant women 20-44 years of age Yan Li, Yizi Meng, Yanxiang Mo, Jin He This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4249882/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gestational weight gain (GWG) and gestational diabetes mellitus (GDM), as two major adverse pregnancy outcomes, could be affected by diet patterns, and GWG also influenced GDM. Therefore, we aimed to explore the four diet quality scores and two adverse pregnancy outcomes in a more macroscopic way. Methods 667 women for GWG part and 333 women for GDM part who were pregnant from the National Health and Nutrition Examination Survey (NHANES), aged 20 to 44 years, were involved in this study, respectively. Four diet quality scores including dietary inflammatory index (DII), dietary Approaches to Stop Hypertension (DASH), Healthy Eating Index-2015 (HEI-2015), and Alternative Healthy Eating Index–2010 (AHEI-2010) were chosen in this study. Results The results of the logistic regression showed that HEI increasing reduced the risk of insufficient GWG (P = 0.002), OR was 0.888(0.825,0.956). A-HEI increasing reduced the risks of insufficient GWG and excessive GWG (P = 0.002, P < 0.001), ORs were 0.840(0.754,0.935) and 0.797(0.729,0.871), respectively. Increased DII was a risk factor for the development of GDM (P = 0.012), OR was 1.931(1.163,3.205), and DASH increasing reduced the risk of GDM (P = 0.028), OR was 0.677(0.479,0.957). These associations were robust after excluding the diabetic patients. For pregnant women with GWG, DASH was negatively associated with the risk of GDM. Conclusion Adherence to healthy dietary pattern was associated with decreased risk of adverse pregnancy outcomes. We recommended advanced maternal age women adhere to HEI-2015 and AHEI-2010 to prevent GWG. For pregnant women with GWG, adherence to DASH was beneficial to GDM. DII HEI-2015 AHEI-2010 DASH Pregnant Women Gestational Weight Gain Gestational Diabetes Figures Figure 1 Introduction Gestational diabetes mellitus (GDM) is the most common and serious complication of pregnancy, affecting about 16.5% of pregnant women globally[ 1 ]. It is worth noting that the incidence of GDM is increasing and will increase with the prevalence of obesity[ 1 , 2 ]. GDM could increase the risk of maternal -infant adverse outcomes, and threaten the health of pregnant women and their offspring[ 3 , 4 ]. Gestational weight gain (GWG), an indicator of maternal fat accumulation as well as uterine, placental, and fetal growth, with the meaning of the nutritional status of a pregnant woman during pregnancy[ 5 ]. Moreover, a higher GWG could also increase the risk of GDM[ 6 , 7 ]. Inflammation has long been hypothesized to play a role in the etiology of adverse pregnancy outcomes, including GDM, hypertensive disorders of pregnancy, and preterm birth[ 8 ]. The underlying mechanism might be that maternal inflammatory molecules could modify the methylation patterns of genes, thereby affecting the birth outcomes[ 9 ]. Diet plays a crucial role in managing gestational diabetes. In general, women with GDM should eat a diet rich in vegetables, whole grains, lean proteins, and healthy fats[ 10 ]. Diet-related inflammation during pregnancy could be causing the increased risk of adverse pregnancy outcomes[ 9 ]. Therefore, adherence to individual healthy eating patterns might be benefit to pregnancy outcomes. Dietary inflammatory index (DII) is a comprehensive index representing the diet-related inflammation, which was extensively explored the association with chronic diseases in the previous studies[ 11 ]. The Dietary Approaches to Stop Hypertension (DASH) diet is recognized as an effective dietary intervention to reduce blood pressure[ 12 ], which also associated other disease except for hypertension. The Healthy Eating Index-2015 (HEI-2015), a diet-quality index, is associated with decreased risk of cancer, cardiovascular disease, and all-cause mortality[ 13 ]. To explore the effect of diet quality in a more macroscopic way, we chose DII, DASH, HEI-2015, AHEI-2010 four diet quality scores to explore the association of adherence to healthy eating patterns with adverse pregnancy outcomes, GDM and GWG. Methods Study Sample Data were pooled from the 1999–2012 annual survey of the National Health and Nutrition Examination Survey (NHANES)[ 14 ] because these cycles investigated the history of live births in pregnant women. 1347 pregnant women aged 20 to 44 years were enrolled, leaving a sample of 763 after excluding missing live birth history, abnormal energy intake, and missing covariates. Two outcome variables (GWG and GDM) and four dietary related indices (HEI, A-HEI, DII, and DASH) missing were subsequently excluded, resulting in four final samples. Details showed in Fig. 1. Figure 1 Sample inclusion and exclusion flowchart. Measures Adverse pregnancy outcomes GWG and GDM were the two outcome variables in this study. According to the American Diabetes Association's One-Step Oral Glucose Tolerance Test (OGTT) strategy, a fasting plasma glucose (FPG) threshold of 5.1 mmol/L was sufficient for a diagnosis of GDM[ 15 ]. Each woman's GWG was categorized as “adequate”, “excessive”, or “insufficient”, based on the difference between each woman's weight measured at the time of the examination and her self-reported pre-pregnancy weight[ 16 ]. Dietary related indices and pattern Four dietary indices such as HEI[ 17 ], A-HEI[ 18 ], DII[ 11 ], and DASH[ 19 ] were used in this study to assess the effects of diet. Due to data discrepancies between NHANES cycles, HEI and A-HEI were calculated using data from the 2005–2012 cycle, and DII and DASH were calculated using data from the 1999–2012 cycle. Details were shown in Supplements. Covariate assessment The family poverty income ratio (Family PIR) was divided into three groups: ≤1.3; 1.3 ~ 3.5; >3.5[ 20 ]. Smoking was divided into three groups: no-smoker, current smoker, and former smoker, based on the questions “Have you smoked at least 100 cigarettes in your entire life” and “Do you now smoke cigarettes”[ 21 ]. Physical activity was divided into three groups by metabolic equivalent (MET): inactive (0 MET·min/week), moderate(< 600 MET·min/week), and vigorous (≥ 600 MET·min/week)[ 22 , 23 ]. The number of live births was divided into three groups: 0 (no live birth history), 1 (one live birth), and ≥ 2 (two or more live births)[ 24 ]. Advanced maternal age was divided into two groups: “Advanced maternal age” group (Age ≥35 years old) and “Not advanced maternal age” group (Age < 35 years old)[ 25 ]. Statistical analysis Four sample groups were analyzed independently. All statistical descriptions and statistical analyses in this study are based on the NHANES complex weighting, thus ensuring a nationally representative sample. Unweighted frequency (N) and weighted percentage (%) were used to describe the categorical variables. Continuous data were expressed using weighted mean ± weighted standard deviation. Binary logistic regression was used to explore the association between dietary related index and adverse pregnancy outcomes. Model 1 was not adjusted for covariates. Model 2 adjusted for age, race, education levels, marital status, and family PIR. Model 3 additionally adjusted for smoking, physical activity, number of live births, and total energy intake over Model 2, Model 3 additionally adjusted GWG for GDM, In addition, considering the effect of a history of previous diabetes, we performed another analysis on pregnant women without a history of previous diabetes to test the sensitivity of the results. Subsequently, considering the effects of advanced maternal age and GWG, we performed interaction analyses. IBM SPSS 24.0 was used in this study. P < 0.05 was considered statistically significant. Results Among 280 participants who investigated GWG and HEI/A-HEI, the mean age was 29.73 ± 0.58 years old, 19.4% were insufficient weight gain, 7.8% were adequate weight gain, and 72.8% were excessive weight gain, as shown in Table 1 . Among 333 participants who investigated GDM and DII/DASH, the mean age was 28.24 ± 0.40 years old, 15.6% with GDM and 84.4% without GDM. Table 1 Basic characteristics of the participants Variables GWG GDM HEI/A-HEI N = 280 DII/DASH N = 667 HEI/A-HEI N = 138 DII/DASH N = 333 Age 29.73 ± 0.58 29.26 ± 0.41 28.07 ± 0.50 28.24 ± 0.40 Race Non-Hispanic White 113(49.3) 297(50.7) 62(54.0) 159(54.1) Other races 167(50.7) 370(49.3) 76(46.0) 174(45.9) Education levels Less than high school 80(21.0) 191(20.5) 40(30.3) 98(22.4) High school diploma 62(19.6) 136(17.7) 28(15.0) 60(12.7) College or above 138(59.5) 340(61.8) 70(54.7) 175(65.0) Marital status Married 190(72.0) 467(70.8) 95(68.5) 238(70.3) Others 90(28.0) 200(29.2) 43(31.5) 95(29.7) Family PIR ≤ 1.3 106(25.4) 244(27.6) 52(26.1) 121(26.0) 1.3–3.5 103(37.2) 245(40.5) 54(45.0) 126(46.9) > 3.5 71(37.4) 178(31.9) 32(28.9) 86(27.1) Smoking No-smoker 199(74.4) 456(67.4) 90(58.6) 218(57.3) Current smoker 32(9.4) 72(11.1) 14(12.2) 32(9.8) Former smoker 49(16.2) 139(21.5) 34(29.2) 83(32.9) Physical activity Inactive 83(29.5) 177(25.3) 42(35.2) 90(27.6) Moderate 98(28.4) 185(24.7) 51(27.7) 93(20.6) Vigorous 99(42.1) 305(49.9) 45(37.0) 150(51.8) Number of live births 0 16(2.9) 48(6.1) 8(3.4) 26(7.9) 1 134(55.4) 314(52.6) 66(51.4) 157(54.1) ≥ 2 130(41.7) 305(41.3) 64(45.2) 150(38.1) Advanced maternal age Yes 33(25.6) 90(21.9) 12(11.9) 40(12.9) No 247(74.4) 577(78.1) 126(88.1) 293(87.1) History of diabetes Yes 4(0.8) 11(0.9) 1(0.2) 5(0.8) No 276(99.2) 656(99.1) 137(99.8) 328(99.2) GDM Yes - - 23(19.9) 44(15.6) No - - 115(80.1) 289(84.4) GWG Insufficient 66(19.4) 138(20.2) 31(20.5) 73(25.1) Adequate 25(7.8) 100(12.7) 12(8.2) 54(12.2) Excessive 189(72.8) 429(67.1) 95(71.3) 206(62.8) Total energy intake (Kcal) 2135.83 ± 66.48 2197.32 ± 40.25 2246.47 ± 114.38 2269.76 ± 59.11 HEI 52.98 ± 1.21 - 51.08 ± 0.64 - AHEI 46.46 ± 0.99 - 46.13 ± 0.89 - DII - 0.19 ± 0.14 - 0.27 ± 0.18 DASH - 2.30 ± 0.08 - 2.16 ± 0.10 Note: The non-total of the component ratios of 100 percent is due to rounding The results of the logistic regression showed that HEI increasing reduced the risk of insufficient GWG ( P = 0.002), OR was 0.888(0.825,0.956), after adjusting for all covariates. A-HEI increasing reduced the risks of insufficient GWG and excessive GWG ( P = 0.002, P < 0.001), ORs were 0.840(0.754,0.935) and 0.797(0.729,0.871), respectively. No statistically significant linear associations between DII or DASH and the prevalence of insufficient or excessive GWG were observed, as shown in Table 2 . However, increased DII was a risk factor for the development of GDM ( P = 0.012), OR was 1.931(1.163,3.205), and DASH increasing reduced the risk of GDM ( P = 0.028), OR was 0.677(0.479,0.957), as shown in Table 3 . After deleting participants with a history of diabetes, none of the above results have changed, as shown in Supplement 5. Table 2 Logistic regressions of GWG with dietary indices Dietary index GWG Model1 a Model2 b Model3 c β P OR (95%CI) β P OR (95%CI) β P OR (95%CI) HEI N = 280 Insufficient -0.074 0.158 0.929(0.838,1.030) -0.098 0.054 0.907(0.820,1.002) -0.092 0.076 0.912(0.824,1.010) Excessive -0.086 0.073 0.918(0.835,1.009) -0.120 0.008 0.887(0.814,0.967) -0.119 0.002 0.888(0.825,0.956) A-HEI N = 280 Insufficient -0.093 0.142 0.912(0.804,1.033) -0.139 0.021 0.870(0.774,0.978) -0.175 0.002 0.840(0.754,0.935) Excessive -0.117 0.012 0.890(0.813,0.974) -0.182 < 0.001 0.834(0.765,0.909) -0.227 < 0.001 0.797(0.729,0.871) DII N = 667 Insufficient 0.076 0.578 1.079(0.822,1.416) 0.027 0.829 1.027(0.804,1.313) 0.176 0.279 1.193(0.865,1.645) Excessive 0.220 0.088 1.246(0.967,1.605) 0.172 0.120 1.188(0.955,1.476) 0.288 0.064 1.333(0.983,1.808) DASH N = 667 Insufficient -0.184 0.261 0.832(0.602,1.150) -0.126 0.455 0.882(0.631,1.232) -0.043 0.778 0.958(0.705,1.300) Excessive -0.286 0.031 0.751(0.579,0.974) -0.242 0.074 0.785(0.602,1.024) -0.237 0.076 0.789(0.607,1.026) Note: a Model1: Dietary index. b Model2: Model1 + age + race + education levels + marital status + family PIR. c Model3: Model2 + smoking + physical activity + number of live births + total energy intake. Table 3 Logistic regressions of GDM with dietary indices Dietary index Model1 a Model2 b Model3 c β P OR (95%CI) β P OR (95%CI) β P OR (95%CI) HEI N = 138 -0.019 0.241 0.981(0.949,1.014) 0.008 0.695 1.008(0.968,1.049) -0.074 0.123 0.929(0.845,1.022) A-HEI N = 138 -0.002 0.968 0.998(0.924,1.079) 0.078 0.086 1.082(0.988,1.184) 0.059 0.314 1.060(0.942,1.194) DII N = 333 0.087 0.578 1.091(0.799,1.489) 0.055 0.750 1.056(0.750,1.487) 0.658 0.012 1.931(1.163,3.205) DASH N = 333 -0.424 0.011 0.654(0.475,0.902) -0.324 0.038 0.724(0.534,0.981) -0.390 0.028 0.677(0.479,0.957) Note: a Model1: Dietary index. b Model2: Model1 + age + race + education levels + marital status + family PIR. c Model3: Model2 + smoking + physical activity + number of live births + total energy intake + GWG. Then considering the high risk of adverse pregnancy outcomes in pregnant women of advanced maternal age, we performed a multiplicative interaction analysis, as shown in Table 4 . The interaction coefficient ( β -interaction) of advanced maternal age on insufficient GWG and excessive GWG per 1 count HEI increase were − 0.460(-0.713, -0.207) ( P -interaction = 0.001) and − 0.446(-0.673, -0.217) ( P -interaction < 0.001), respectively. The β -interaction of advanced maternal age on insufficient GWG and excessive GWG per 1 count A-HEI increase were − 0.307(-0.555, -0.057) ( P -interaction = 0.017) and − 0.288(-0.514, -0.062) ( P -interaction = 0.014), respectively. These suggested negative interaction on the multiplicative scale. However, for GDM, we did not find an interaction between dietary index and advanced maternal age, as shown in Supplement 6. Table 4 Interaction results on the multiplicative scale for the effect of advanced maternal age on GWG per 1 count diet quality scores increase Advanced maternal age*Diet quality scores Sample size(N) GWG β -interaction(95%CI) P -interaction Advanced maternal age*HEI 280 Insufficient -0.460(-0.713, -0.207) 0.001 Excessive -0.446(-0.673, -0.217) < 0.001 Advanced maternal age*A-HEI 280 Insufficient -0.307(-0.555, -0.057) 0.017 Excessive -0.288(-0.514, -0.062) 0.014 Advanced maternal age*DII 667 Insufficient 0.008(-0.832, 0.847) 0.986 Excessive -0.316(-0.911, 0.281) 0.296 Advanced maternal age*DASH 667 Insufficient 0.057(-0.681, 0.794) 0.878 Excessive 0.369(-0.260, 0.999) 0.247 Note: Adjusted for race, education levels, marital status, family PIR, smoking, physical activity, number of live births, and total energy intake. Subsequently, considering the effects of GWG for GDM, we performed a multiplicative interaction analysis, as shown in Table 5 . The β -interaction of insufficient GWG and excessive GWG on GDM per 1 count DASH increase were − 2.263(-3.912, -0.625) ( P -interaction = 0.008) and − 2.137(-3.772, -0.498) ( P -interaction = 0.012), respectively. Table 5 Interaction results on the multiplicative scale for the effect of GWG on GWG per 1 count diet quality scores increase GWG*Diet quality scores Sample size(N) β -interaction(95%CI) P -interaction Insufficient*HEI 138 -0.056(-0.132, 0.021) 0.146 Excessive*HEI -0.046(-0.248, 0.157) 0.644 Insufficient*A-HEI 138 -0.006(-0.202, 0.191) 0.949 Excessive*A-HEI -0.282(-0.726, 0.160) 0.199 Insufficient*DII 333 0.034(-0.860, 0.928) 0.939 Excessive*DII -0.270(-1.056, 0.513) 0.491 Insufficient*DASH 333 -2.263(-3.912, -0.625) 0.008 Excessive*DASH -2.137(-3.772, -0.498) 0.012 Note: Adjusted for age, race, education levels, marital status, family PIR, smoking, physical activity, number of live births, and total energy intake. Discussion Diet was strongly associated with adverse pregnancy outcomes; thus, we have explored the four diet quality scores and the two adverse pregnancy outcomes by a nationally representative population. The mainly founding are as follow: Firstly, higher HEI-2015 and AHEI-2010 were associated with lower risk of GWG, especially in advanced maternal age; adherence of DASH and anti-inflammatory diet were associated with decreased risk of GDM. Secondly, for pregnant women with GWG, adherence of DASH was benefit to preventing GDM. Thirdly, the associations between diet quality scores and adverse pregnancy outcomes were robust after excluding diabetic patients. The prevalence of multiple adverse pregnancy outcomes among pregnant women with GDM had been increased in the United States from 2014 to 2020[ 26 ]. Previous studies found that diet was strongly associated with pregnancy outcomes. The association between diet quality and GWG were not uniform in the previous studies. A German cross-sectional study not found the association between HEI and GWG[ 27 ]. However, the Pregnancy Environment and Lifestyle Study (PETALS) suggested that diet quality measured by the HEI-2010 is associated with excessive GWG[ 28 ], which was consistent with our study. Also, a prospective cohort study found that pregnant women with a poor or fair diet quality could gain 2 kg than those pregnant women with higher-quality diets[ 29 ]. Stillbirth risk increases with increasing maternal age, the prevalence of adverse pregnancy outcomes was higher in advanced maternal age women[ 30 ]. The association between HEI/AHEI and GWG was stronger in the advanced maternal in our study. Therefore, we suggested that adherence healthy diet pattern according to the HEI-2015 and AHEI-2010 to decrease the risk of GWG, especially for advanced maternal age. Although there was no association between DII and GWG in this study, other study has been found higher inflammatory level associated with higher GWG rate in the pregnant women[ 31 ]. Thus, pregnant women are not advised to ignore the diet-related inflammation. All pregnant women should be encouraged to consume a healthier diet throughout their pregnancy to prevent adverse maternal and fetal outcomes due to the development of GWG[ 32 ]. Anti-inflammatory diet and DASH were recommended to adhere to prevent GDM during pregnancy based on our study. A Finnish study revealed that a high inflammatory potential of the diet was associated with an increased risk of GDM[ 33 ], which was consistent with our founding. Dietary fats were regarded as one reason factor between inflammation and GDM, they could increase the presence of chronic, systemic inflammation due to total fat, SFAs, and trans fatty acids have a high inflammatory potential[ 11 , 34 ]. DASH eating plan, which was rich in fruits, vegetables, and low-fat dairy products, is a low-GI low-energy-dense diet, used to lower blood pressure, initially[ 35 ]. However, it has also been reported playing an important role in diabetes and the metabolic syndrome[ 36 , 37 ]. A meta-analysis concluded that diets resembling DASH diet in early pregnancy were associated with lower risks or odds of GDM[ 38 ]. Moreover, other high-quality diets were also recommended for pregnant women to adhere. It was reported that a high AHEI 2010 score was associated with a reduced risk of GDM by 19% or 46%[ 39 , 40 ]. Avoiding excessive GWG also might be a potential strategy of prevention of GDM for pregnant women[ 41 ]. A previous study from the Japanese Birth Cohort pointed that the effect of diet on the occurrence of GDM depends on pre-pregnancy BMI[ 42 ]. Our study suggested that for pregnant women with GWG, it was more important to adherence of DASH eating plan to prevent of GDM. This result may be useful in providing individualized preconception counselling based on maternal circumstances. There are also some limitations in this study. Firstly, it was a serial cross-sectional study, might be some self-reported bias. Secondly, the sample size might be a little small, thereby the subgroup analysis may be difficult to achieve. Conclusions Adherence of healthy dietary pattern associated with decreased risk of adverse pregnancy outcomes. We recommended advanced maternal age women to adherence of HEI-2015 and AHEI-2010 to prevent GWG. For pregnant women with GWG, adherence of DASH was benefit to GDM. Abbreviations Gestational weight gain (GWG) Gestational diabetes mellitus (GDM) National Health and Nutrition Examination Survey (NHANES) Dietary inflammatory index (DII) Dietary Approaches to Stop Hypertension (DASH) Healthy Eating Index-2015 (HEI-2015) Alternative Healthy Eating Index–2010 (AHEI-2010) Family poverty income ratio (Family PIR) Declarations Ethics approval and consent to participate: Written informed consent was obtained from each participant before participation in this study. ID: NCHS IRB/ERB Protocol #98-12、Protocol #2005-06、Protocol #2011-17. Consent for publication: Not applicable. Availability of data and materials: The data underlying this article are available in the National Health and Nutrition Examination Survey (NHANES) at https://www.cdc.gov/nchs/nhanes/index.htm. Competing interests: All authors have no potential conflicts of interest. Funding: Not applicable. Authors' contributions: YL and JH made the study design; YL and JH conducted the study; YL and YiziM analyzed the data and wrote the manuscript; YL, YanxingM, and JH participated amending the manuscript. All authors agreed with the final version of the manuscript. Acknowledgements: Not applicable. References Plows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci 2018, 19(11). Alberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, Erenbourg A, Ronfani L, Maso G, Multicentre Study Group on Mode of Delivery in Friuli Venezia G. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. BMC Pregnancy Childbirth. 2014;14:23. Waters TP, Dyer AR, Scholtens DM, Dooley SL, Herer E, Lowe LP, Oats JJ, Persson B, Sacks DA, Metzger BE, et al. 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Risk of Adverse Pregnancy Outcomes Among Pregnant Individuals With Gestational Diabetes by Race and Ethnicity in the United States, 2014–2020. JAMA. 2022;327(14):1356–67. Ehrhardt C, Deibert C, Flock A, Merz WM, Gembruch U, Bockler A, Dotsch J, Joisten C, Ferrari N. Impact of Diet Quality during Pregnancy on Gestational Weight Gain and Selected Adipokines-Results of a German Cross-Sectional Study. Nutrients 2022, 14(7). Liu EF, Zhu Y, Ferrara A, Hedderson MM. Dietary Quality Indices in Early Pregnancy and Rate of Gestational Weight Gain among a Prospective Multi-Racial and Ethnic Cohort. Nutrients 2023, 15(4). Augustin H, Winkvist A, Barebring L. Poor Dietary Quality is Associated with Low Adherence to Gestational Weight Gain Recommendations among Women in Sweden. Nutrients 2020, 12(2). Lean SC, Derricott H, Jones RL, Heazell AEP. Advanced maternal age and adverse pregnancy outcomes: A systematic review and meta-analysis. PLoS ONE. 2017;12(10):e0186287. Perng W, Rifas-Shiman SL, Rich-Edwards JW, Stuebe AM, Oken E. Inflammation and weight gain in reproductive-aged women. Ann Hum Biol. 2016;43(1):91–5. Ancira-Moreno M, Vadillo-Ortega F, Rivera-Dommarco JA, Sanchez BN, Pasteris J, Batis C, Castillo-Castrejon M, O'Neill MS. Gestational weight gain trajectories over pregnancy and their association with maternal diet quality: Results from the PRINCESA cohort. Nutrition. 2019;65:158–66. Pajunen L, Korkalo L, Koivuniemi E, Houttu N, Pellonpera O, Mokkala K, Shivappa N, Hebert JR, Vahlberg T, Tertti K, et al. A healthy dietary pattern with a low inflammatory potential reduces the risk of gestational diabetes mellitus. Eur J Nutr. 2022;61(3):1477–90. Minihane AM, Vinoy S, Russell WR, Baka A, Roche HM, Tuohy KM, Teeling JL, Blaak EE, Fenech M, Vauzour D, et al. Low-grade inflammation, diet composition and health: current research evidence and its translation. Br J Nutr. 2015;114(7):999–1012. Vollmer WM, Sacks FM, Ard J, Appel LJ, Bray GA, Simons-Morton DG, Conlin PR, Svetkey LP, Erlinger TP, Moore TJ, et al. Effects of diet and sodium intake on blood pressure: subgroup analysis of the DASH-sodium trial. Ann Intern Med. 2001;135(12):1019–28. Azadbakht L, Fard NR, Karimi M, Baghaei MH, Surkan PJ, Rahimi M, Esmaillzadeh A, Willett WC. Effects of the Dietary Approaches to Stop Hypertension (DASH) eating plan on cardiovascular risks among type 2 diabetic patients: a randomized crossover clinical trial. Diabetes Care. 2011;34(1):55–7. Azadbakht L, Mirmiran P, Esmaillzadeh A, Azizi T, Azizi F. Beneficial effects of a Dietary Approaches to Stop Hypertension eating plan on features of the metabolic syndrome. Diabetes Care. 2005;28(12):2823–31. Mijatovic-Vukas J, Capling L, Cheng S, Stamatakis E, Louie J, Cheung NW, Markovic T, Ross G, Senior A, Brand-Miller JC et al. Associations of Diet and Physical Activity with Risk for Gestational Diabetes Mellitus: A Systematic Review and Meta-Analysis. Nutrients 2018, 10(6). Zhang C, Tobias DK, Chavarro JE, Bao W, Wang D, Ley SH, Hu FB. Adherence to healthy lifestyle and risk of gestational diabetes mellitus: prospective cohort study. BMJ. 2014;349:g5450. Tobias DK, Zhang C, Chavarro J, Bowers K, Rich-Edwards J, Rosner B, Mozaffarian D, Hu FB. Prepregnancy adherence to dietary patterns and lower risk of gestational diabetes mellitus. Am J Clin Nutr. 2012;96(2):289–95. Brunner S, Stecher L, Ziebarth S, Nehring I, Rifas-Shiman SL, Sommer C, Hauner H, von Kries R. Excessive gestational weight gain prior to glucose screening and the risk of gestational diabetes: a meta-analysis. Diabetologia. 2015;58(10):2229–37. Kyozuka H, Murata T, Isogami H, Imaizumi K, Fukuda T, Yamaguchi A, Yasuda S, Sato A, Ogata Y, Hosoya M et al. Preconception Dietary Inflammatory Index and Risk of Gestational Diabetes Mellitus Based on Maternal Body Mass Index: Findings from a Japanese Birth Cohort Study. Nutrients 2022, 14(19). Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4249882","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290149360,"identity":"2045aecb-214d-4210-9fde-d24cd1bc050b","order_by":0,"name":"Yan Li","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":290149361,"identity":"68ed0838-2d8a-4183-85cb-4e7ed8f79ffc","order_by":1,"name":"Yizi Meng","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yizi","middleName":"","lastName":"Meng","suffix":""},{"id":290149362,"identity":"a586729d-8964-4366-b343-f5820c87da56","order_by":2,"name":"Yanxiang Mo","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yanxiang","middleName":"","lastName":"Mo","suffix":""},{"id":290149363,"identity":"2c2cbbec-04ff-41c1-8176-940dcf6e6ad1","order_by":3,"name":"Jin He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACAwaGhAMMFWwyII4ECVrOsPGQpIWBgbGNgQQt5uwNDw8XzuPjMTjAfPA2D4NdHkEtlj0HEg7P3MYG1MKWbM3DkFxM2GE3EhIO84K18JhJ8zAcSGwgqOX+A6CWOSAt/N+I1HKDAailAWwLG5FazgAdxnOMjUfyMJux5RyDZCK0HD+T/Jmn5pgc3/HmhzfeVNgR1sLAwJMAJI4xMDCDTSCsHgjYDwCJGqKUjoJRMApGwQgFAKWcOW+4E/RCAAAAAElFTkSuQmCC","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2024-04-11 02:44:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4249882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4249882/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54997290,"identity":"403c678f-3217-4525-98d3-52d30c089050","added_by":"auto","created_at":"2024-04-19 18:10:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":112839,"visible":true,"origin":"","legend":"\u003cp\u003eSample inclusion and exclusion flowchart.\u003c/p\u003e","description":"","filename":"Figure1Sampleinclusionandexclusionflowchart..jpg","url":"https://assets-eu.researchsquare.com/files/rs-4249882/v1/ca2f982ee6ac079432952bcf.jpg"},{"id":54997977,"identity":"1820e5be-c57c-44ca-9782-46ae1cbce432","added_by":"auto","created_at":"2024-04-19 18:18:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":457704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4249882/v1/3e77637c-a49f-4852-b67e-fd21086e434a.pdf"},{"id":54997289,"identity":"0affba64-d09f-4e09-9bee-ab327de6854d","added_by":"auto","created_at":"2024-04-19 18:10:16","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":149077,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-4249882/v1/b07b9011468e4eb39a648892.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of diet during pregnancy with adverse pregnancy outcomes: a cross-sectional study of pregnant women 20-44 years of age","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM) is the most common and serious complication of pregnancy, affecting about 16.5% of pregnant women globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is worth noting that the incidence of GDM is increasing and will increase with the prevalence of obesity[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. GDM could increase the risk of maternal -infant adverse outcomes, and threaten the health of pregnant women and their offspring[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Gestational weight gain (GWG), an indicator of maternal fat accumulation as well as uterine, placental, and fetal growth, with the meaning of the nutritional status of a pregnant woman during pregnancy[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Moreover, a higher GWG could also increase the risk of GDM[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInflammation has long been hypothesized to play a role in the etiology of adverse pregnancy outcomes, including GDM, hypertensive disorders of pregnancy, and preterm birth[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The underlying mechanism might be that maternal inflammatory molecules could modify the methylation patterns of genes, thereby affecting the birth outcomes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Diet plays a crucial role in managing gestational diabetes. In general, women with GDM should eat a diet rich in vegetables, whole grains, lean proteins, and healthy fats[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Diet-related inflammation during pregnancy could be causing the increased risk of adverse pregnancy outcomes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, adherence to individual healthy eating patterns might be benefit to pregnancy outcomes.\u003c/p\u003e \u003cp\u003eDietary inflammatory index (DII) is a comprehensive index representing the diet-related inflammation, which was extensively explored the association with chronic diseases in the previous studies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Dietary Approaches to Stop Hypertension (DASH) diet is recognized as an effective dietary intervention to reduce blood pressure[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which also associated other disease except for hypertension. The Healthy Eating Index-2015 (HEI-2015), a diet-quality index, is associated with decreased risk of cancer, cardiovascular disease, and all-cause mortality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To explore the effect of diet quality in a more macroscopic way, we chose DII, DASH, HEI-2015, AHEI-2010 four diet quality scores to explore the association of adherence to healthy eating patterns with adverse pregnancy outcomes, GDM and GWG.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Sample\u003c/h2\u003e \u003cp\u003eData were pooled from the 1999\u0026ndash;2012 annual survey of the National Health and Nutrition Examination Survey (NHANES)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] because these cycles investigated the history of live births in pregnant women. 1347 pregnant women aged 20 to 44 years were enrolled, leaving a sample of 763 after excluding missing live birth history, abnormal energy intake, and missing covariates. Two outcome variables (GWG and GDM) and four dietary related indices (HEI, A-HEI, DII, and DASH) missing were subsequently excluded, resulting in four final samples. Details showed in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure 1\u003c/b\u003e Sample inclusion and exclusion flowchart.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eAdverse pregnancy outcomes\u003c/h2\u003e \u003cp\u003eGWG and GDM were the two outcome variables in this study. According to the American Diabetes Association's One-Step Oral Glucose Tolerance Test (OGTT) strategy, a fasting plasma glucose (FPG) threshold of 5.1 mmol/L was sufficient for a diagnosis of GDM[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Each woman's GWG was categorized as \u0026ldquo;adequate\u0026rdquo;, \u0026ldquo;excessive\u0026rdquo;, or \u0026ldquo;insufficient\u0026rdquo;, based on the difference between each woman's weight measured at the time of the examination and her self-reported pre-pregnancy weight[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDietary related indices and pattern\u003c/h2\u003e \u003cp\u003eFour dietary indices such as HEI[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], A-HEI[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], DII[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and DASH[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] were used in this study to assess the effects of diet. Due to data discrepancies between NHANES cycles, HEI and A-HEI were calculated using data from the 2005\u0026ndash;2012 cycle, and DII and DASH were calculated using data from the 1999\u0026ndash;2012 cycle. Details were shown in Supplements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariate assessment\u003c/h2\u003e \u003cp\u003eThe family poverty income ratio (Family PIR) was divided into three groups: \u0026le;1.3; 1.3\u0026thinsp;~\u0026thinsp;3.5; \u0026gt;3.5[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Smoking was divided into three groups: no-smoker, current smoker, and former smoker, based on the questions \u0026ldquo;Have you smoked at least 100 cigarettes in your entire life\u0026rdquo; and \u0026ldquo;Do you now smoke cigarettes\u0026rdquo;[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Physical activity was divided into three groups by metabolic equivalent (MET): inactive (0 MET\u0026middot;min/week), moderate(\u0026lt;\u0026thinsp;600 MET\u0026middot;min/week), and vigorous (\u0026ge;\u0026thinsp;600 MET\u0026middot;min/week)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The number of live births was divided into three groups: 0 (no live birth history), 1 (one live birth), and \u0026ge;\u0026thinsp;2 (two or more live births)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Advanced maternal age was divided into two groups: \u0026ldquo;Advanced maternal age\u0026rdquo; group (Age \u0026ge;35 years old) and \u0026ldquo;Not advanced maternal age\u0026rdquo; group (Age\u0026thinsp;\u0026lt;\u0026thinsp;35 years old)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eFour sample groups were analyzed independently. All statistical descriptions and statistical analyses in this study are based on the NHANES complex weighting, thus ensuring a nationally representative sample. Unweighted frequency (N) and weighted percentage (%) were used to describe the categorical variables. Continuous data were expressed using weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;weighted standard deviation. Binary logistic regression was used to explore the association between dietary related index and adverse pregnancy outcomes. Model 1 was not adjusted for covariates. Model 2 adjusted for age, race, education levels, marital status, and family PIR. Model 3 additionally adjusted for smoking, physical activity, number of live births, and total energy intake over Model 2, Model 3 additionally adjusted GWG for GDM, In addition, considering the effect of a history of previous diabetes, we performed another analysis on pregnant women without a history of previous diabetes to test the sensitivity of the results. Subsequently, considering the effects of advanced maternal age and GWG, we performed interaction analyses. IBM SPSS 24.0 was used in this study. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong 280 participants who investigated GWG and HEI/A-HEI, the mean age was 29.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 years old, 19.4% were insufficient weight gain, 7.8% were adequate weight gain, and 72.8% were excessive weight gain, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among 333 participants who investigated GDM and DII/DASH, the mean age was 28.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 years old, 15.6% with GDM and 84.4% without GDM.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic characteristics of the participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHEI/A-HEI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;280\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDII/DASH\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;667\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHEI/A-HEI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;138\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDII/DASH\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;333\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e159(54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167(50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370(49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e174(45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEducation levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40(30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98(22.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136(17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60(12.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138(59.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e340(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70(54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175(65.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190(72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e467(70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95(68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e238(70.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90(28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43(31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95(29.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFamily PIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106(25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244(27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52(26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e121(26.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245(40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54(45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e126(46.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86(27.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199(74.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456(67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90(58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e218(57.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32(9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83(32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90(27.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185(24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51(27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93(20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVigorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305(49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150(51.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of live births\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26(7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134(55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e314(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157(54.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130(41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305(41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64(45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e150(38.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAdvanced maternal age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90(21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40(12.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247(74.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577(78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126(88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e293(87.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHistory of diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e276(99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e656(99.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137(99.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328(99.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44(15.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115(80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e289(84.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGWG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73(25.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdequate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54(12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189(72.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429(67.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e206(62.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTotal energy intake (Kcal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2135.83\u0026thinsp;\u0026plusmn;\u0026thinsp;66.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2197.32\u0026thinsp;\u0026plusmn;\u0026thinsp;40.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2246.47\u0026thinsp;\u0026plusmn;\u0026thinsp;114.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2269.76\u0026thinsp;\u0026plusmn;\u0026thinsp;59.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAHEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNote: The non-total of the component ratios of 100 percent is due to rounding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the logistic regression showed that HEI increasing reduced the risk of insufficient GWG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), OR was 0.888(0.825,0.956), after adjusting for all covariates. A-HEI increasing reduced the risks of insufficient GWG and excessive GWG (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ORs were 0.840(0.754,0.935) and 0.797(0.729,0.871), respectively. No statistically significant linear associations between DII or DASH and the prevalence of insufficient or excessive GWG were observed, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, increased DII was a risk factor for the development of GDM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), OR was 1.931(1.163,3.205), and DASH increasing reduced the risk of GDM (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028), OR was 0.677(0.479,0.957), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. After deleting participants with a history of diabetes, none of the above results have changed, as shown in Supplement 5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regressions of GWG with dietary indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDietary index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eModel1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eModel2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eModel3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHEI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.929(0.838,1.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.907(0.820,1.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.912(0.824,1.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.918(0.835,1.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.887(0.814,0.967)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.888(0.825,0.956)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eA-HEI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.912(0.804,1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.870(0.774,0.978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.840(0.754,0.935)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.890(0.813,0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.834(0.765,0.909)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.797(0.729,0.871)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDII\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.079(0.822,1.416)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.027(0.804,1.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.193(0.865,1.645)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.246(0.967,1.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.188(0.955,1.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.333(0.983,1.808)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDASH\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.832(0.602,1.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.882(0.631,1.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.958(0.705,1.300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.751(0.579,0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.785(0.602,1.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.789(0.607,1.026)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eNote: a Model1: Dietary index.\u003c/p\u003e \u003cp\u003eb Model2: Model1\u0026thinsp;+\u0026thinsp;age\u0026thinsp;+\u0026thinsp;race\u0026thinsp;+\u0026thinsp;education levels\u0026thinsp;+\u0026thinsp;marital status\u0026thinsp;+\u0026thinsp;family PIR.\u003c/p\u003e \u003cp\u003ec Model3: Model2\u0026thinsp;+\u0026thinsp;smoking\u0026thinsp;+\u0026thinsp;physical activity\u0026thinsp;+\u0026thinsp;number of live births\u0026thinsp;+\u0026thinsp;total energy intake.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regressions of GDM with dietary indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDietary index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eModel2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eModel3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.981(0.949,1.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.008(0.968,1.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.929(0.845,1.022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-HEI\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998(0.924,1.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.082(0.988,1.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.060(0.942,1.194)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDII\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.091(0.799,1.489)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.056(0.750,1.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.931(1.163,3.205)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDASH\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654(0.475,0.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.724(0.534,0.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.677(0.479,0.957)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003eNote: a Model1: Dietary index.\u003c/p\u003e \u003cp\u003eb Model2: Model1\u0026thinsp;+\u0026thinsp;age\u0026thinsp;+\u0026thinsp;race\u0026thinsp;+\u0026thinsp;education levels\u0026thinsp;+\u0026thinsp;marital status\u0026thinsp;+\u0026thinsp;family PIR.\u003c/p\u003e \u003cp\u003ec Model3: Model2\u0026thinsp;+\u0026thinsp;smoking\u0026thinsp;+\u0026thinsp;physical activity\u0026thinsp;+\u0026thinsp;number of live births\u0026thinsp;+\u0026thinsp;total energy intake\u0026thinsp;+\u0026thinsp;GWG.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThen considering the high risk of adverse pregnancy outcomes in pregnant women of advanced maternal age, we performed a multiplicative interaction analysis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The interaction coefficient (\u003cem\u003eβ\u003c/em\u003e-interaction) of advanced maternal age on insufficient GWG and excessive GWG per 1 count HEI increase were \u0026minus;\u0026thinsp;0.460(-0.713, -0.207) (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.001) and \u0026minus;\u0026thinsp;0.446(-0.673, -0.217) (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. The \u003cem\u003eβ\u003c/em\u003e-interaction of advanced maternal age on insufficient GWG and excessive GWG per 1 count A-HEI increase were \u0026minus;\u0026thinsp;0.307(-0.555, -0.057) (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.017) and \u0026minus;\u0026thinsp;0.288(-0.514, -0.062) (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.014), respectively. These suggested negative interaction on the multiplicative scale. However, for GDM, we did not find an interaction between dietary index and advanced maternal age, as shown in Supplement 6.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction results on the multiplicative scale for the effect of advanced maternal age on GWG per 1 count diet quality scores increase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced maternal age*Diet quality scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e-interaction(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdvanced maternal age*HEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.460(-0.713, -0.207)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.446(-0.673, -0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdvanced maternal age*A-HEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.307(-0.555, -0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.288(-0.514, -0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdvanced maternal age*DII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008(-0.832, 0.847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.316(-0.911, 0.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdvanced maternal age*DASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsufficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057(-0.681, 0.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.369(-0.260, 0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote: Adjusted for race, education levels, marital status, family PIR, smoking, physical activity, number of live births, and total energy intake.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubsequently, considering the effects of GWG for GDM, we performed a multiplicative interaction analysis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The \u003cem\u003eβ\u003c/em\u003e-interaction of insufficient GWG and excessive GWG on GDM per 1 count DASH increase were \u0026minus;\u0026thinsp;2.263(-3.912, -0.625) (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.008) and \u0026minus;\u0026thinsp;2.137(-3.772, -0.498) (\u003cem\u003eP\u003c/em\u003e-interaction\u0026thinsp;=\u0026thinsp;0.012), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInteraction results on the multiplicative scale for the effect of GWG on GWG per 1 count diet quality scores increase\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWG*Diet quality scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample size(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e-interaction(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient*HEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.056(-0.132, 0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive*HEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.046(-0.248, 0.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient*A-HEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.006(-0.202, 0.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive*A-HEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.282(-0.726, 0.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient*DII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034(-0.860, 0.928)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive*DII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.270(-1.056, 0.513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient*DASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.263(-3.912, -0.625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive*DASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.137(-3.772, -0.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: Adjusted for age, race, education levels, marital status, family PIR, smoking, physical activity, number of live births, and total energy intake.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDiet was strongly associated with adverse pregnancy outcomes; thus, we have explored the four diet quality scores and the two adverse pregnancy outcomes by a nationally representative population. The mainly founding are as follow: Firstly, higher HEI-2015 and AHEI-2010 were associated with lower risk of GWG, especially in advanced maternal age; adherence of DASH and anti-inflammatory diet were associated with decreased risk of GDM. Secondly, for pregnant women with GWG, adherence of DASH was benefit to preventing GDM. Thirdly, the associations between diet quality scores and adverse pregnancy outcomes were robust after excluding diabetic patients.\u003c/p\u003e \u003cp\u003eThe prevalence of multiple adverse pregnancy outcomes among pregnant women with GDM had been increased in the United States from 2014 to 2020[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Previous studies found that diet was strongly associated with pregnancy outcomes. The association between diet quality and GWG were not uniform in the previous studies. A German cross-sectional study not found the association between HEI and GWG[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the Pregnancy Environment and Lifestyle Study (PETALS) suggested that diet quality measured by the HEI-2010 is associated with excessive GWG[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which was consistent with our study. Also, a prospective cohort study found that pregnant women with a poor or fair diet quality could gain 2 kg than those pregnant women with higher-quality diets[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Stillbirth risk increases with increasing maternal age, the prevalence of adverse pregnancy outcomes was higher in advanced maternal age women[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The association between HEI/AHEI and GWG was stronger in the advanced maternal in our study. Therefore, we suggested that adherence healthy diet pattern according to the HEI-2015 and AHEI-2010 to decrease the risk of GWG, especially for advanced maternal age. Although there was no association between DII and GWG in this study, other study has been found higher inflammatory level associated with higher GWG rate in the pregnant women[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, pregnant women are not advised to ignore the diet-related inflammation. All pregnant women should be encouraged to consume a healthier diet throughout their pregnancy to prevent adverse maternal and fetal outcomes due to the development of GWG[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnti-inflammatory diet and DASH were recommended to adhere to prevent GDM during pregnancy based on our study. A Finnish study revealed that a high inflammatory potential of the diet was associated with an increased risk of GDM[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which was consistent with our founding. Dietary fats were regarded as one reason factor between inflammation and GDM, they could increase the presence of chronic, systemic inflammation due to total fat, SFAs, and trans fatty acids have a high inflammatory potential[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. DASH eating plan, which was rich in fruits, vegetables, and low-fat dairy products, is a low-GI low-energy-dense diet, used to lower blood pressure, initially[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, it has also been reported playing an important role in diabetes and the metabolic syndrome[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A meta-analysis concluded that diets resembling DASH diet in early pregnancy were associated with lower risks or odds of GDM[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Moreover, other high-quality diets were also recommended for pregnant women to adhere. It was reported that a high AHEI 2010 score was associated with a reduced risk of GDM by 19% or 46%[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAvoiding excessive GWG also might be a potential strategy of prevention of GDM for pregnant women[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A previous study from the Japanese Birth Cohort pointed that the effect of diet on the occurrence of GDM depends on pre-pregnancy BMI[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our study suggested that for pregnant women with GWG, it was more important to adherence of DASH eating plan to prevent of GDM. This result may be useful in providing individualized preconception counselling based on maternal circumstances.\u003c/p\u003e \u003cp\u003eThere are also some limitations in this study. Firstly, it was a serial cross-sectional study, might be some self-reported bias. Secondly, the sample size might be a little small, thereby the subgroup analysis may be difficult to achieve.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAdherence of healthy dietary pattern associated with decreased risk of adverse pregnancy outcomes. We recommended advanced maternal age women to adherence of HEI-2015 and AHEI-2010 to prevent GWG. For pregnant women with GWG, adherence of DASH was benefit to GDM.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGestational weight gain (GWG)\u003c/p\u003e\n\u003cp\u003eGestational diabetes mellitus (GDM)\u003c/p\u003e\n\u003cp\u003eNational Health and Nutrition Examination Survey (NHANES)\u003c/p\u003e\n\u003cp\u003eDietary inflammatory index (DII)\u003c/p\u003e\n\u003cp\u003eDietary Approaches to Stop Hypertension (DASH)\u003c/p\u003e\n\u003cp\u003eHealthy Eating Index-2015 (HEI-2015)\u003c/p\u003e\n\u003cp\u003eAlternative Healthy Eating Index\u0026ndash;2010 (AHEI-2010)\u003c/p\u003e\n\u003cp\u003eFamily poverty income ratio (Family PIR)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from each participant before participation in this study. ID: NCHS IRB/ERB Protocol #98-12、Protocol #2005-06、Protocol #2011-17.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article are available in the National Health and Nutrition Examination Survey (NHANES) at https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL and JH made the study design; YL and JH conducted the study; YL and YiziM analyzed the data and wrote the manuscript; YL, YanxingM, and JH participated amending the manuscript. All authors agreed with the final version of the manuscript.\u0026nbsp;\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePlows JF, Stanley JL, Baker PN, Reynolds CM, Vickers MH. The Pathophysiology of Gestational Diabetes Mellitus. Int J Mol Sci 2018, 19(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberico S, Montico M, Barresi V, Monasta L, Businelli C, Soini V, Erenbourg A, Ronfani L, Maso G, Multicentre Study Group on Mode of Delivery in Friuli Venezia G. The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study. 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Preconception Dietary Inflammatory Index and Risk of Gestational Diabetes Mellitus Based on Maternal Body Mass Index: Findings from a Japanese Birth Cohort Study. \u003cem\u003eNutrients\u003c/em\u003e 2022, 14(19).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DII, HEI-2015, AHEI-2010, DASH, Pregnant Women, Gestational Weight Gain, Gestational Diabetes","lastPublishedDoi":"10.21203/rs.3.rs-4249882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4249882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGestational weight gain (GWG) and gestational diabetes mellitus (GDM), as two major adverse pregnancy outcomes, could be affected by diet patterns, and GWG also influenced GDM. Therefore, we aimed to explore the four diet quality scores and two adverse pregnancy outcomes in a more macroscopic way.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e667 women for GWG part and 333 women for GDM part who were pregnant from the National Health and Nutrition Examination Survey (NHANES), aged 20 to 44 years, were involved in this study, respectively. Four diet quality scores including dietary inflammatory index (DII), dietary Approaches to Stop Hypertension (DASH), Healthy Eating Index-2015 (HEI-2015), and Alternative Healthy Eating Index\u0026ndash;2010 (AHEI-2010) were chosen in this study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of the logistic regression showed that HEI increasing reduced the risk of insufficient GWG (P\u0026thinsp;=\u0026thinsp;0.002), OR was 0.888(0.825,0.956). A-HEI increasing reduced the risks of insufficient GWG and excessive GWG (P\u0026thinsp;=\u0026thinsp;0.002, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ORs were 0.840(0.754,0.935) and 0.797(0.729,0.871), respectively. Increased DII was a risk factor for the development of GDM (P\u0026thinsp;=\u0026thinsp;0.012), OR was 1.931(1.163,3.205), and DASH increasing reduced the risk of GDM (P\u0026thinsp;=\u0026thinsp;0.028), OR was 0.677(0.479,0.957). These associations were robust after excluding the diabetic patients. For pregnant women with GWG, DASH was negatively associated with the risk of GDM.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAdherence to healthy dietary pattern was associated with decreased risk of adverse pregnancy outcomes. We recommended advanced maternal age women adhere to HEI-2015 and AHEI-2010 to prevent GWG. For pregnant women with GWG, adherence to DASH was beneficial to GDM.\u003c/p\u003e","manuscriptTitle":"Association of diet during pregnancy with adverse pregnancy outcomes: a cross-sectional study of pregnant women 20-44 years of age","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-19 18:10:10","doi":"10.21203/rs.3.rs-4249882/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c797e41-9b2e-4f17-a5b2-888d43570f76","owner":[],"postedDate":"April 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-19T18:10:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-19 18:10:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4249882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4249882","identity":"rs-4249882","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}