The association of diet quality scores with the adverse pregnancy outcomes

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The association of diet quality scores with the adverse pregnancy outcomes

https://doi.org/10.21203/rs.3.rs-3241312/v1

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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. 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 In-dex-2015 (HEI-2015), and Alternative Healthy Eating Index–2010 (AHEI-2010) were chosen in this study. We found that higher HEI-2015 and AHEI-2010 were associated with lower risk of GWG, especially for advanced maternal age. Lower DII and higher DASH were associated with lower risk of GDM. These associations were robust after excluding the diabetic patients. For pregnant women with GWG, DASH was negatively associated with the risk of GDM. Summarily, 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.

Health sciences/Medical research/Epidemiology

Health sciences/Diseases/Endocrine system and metabolic diseases/Diabetes/Gestational diabetes

DII

HEI-2015

AHEI-2010

DASH

Pregnant Women

Gestational Weight Gain

Gestational Diabetes

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 pre-term birth[8]. The underlying mechanism might be that maternal inflammatory molecules could modify the methylation patterns of genes, thereby affecting the birth out-comes[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 di-et-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.

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.

Measures

Adverse pregnancy outcomes. GWG and GDM were the two outcome variables in this study. 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[15]. 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[16].

Diet quality scores. Four diet quality scores including 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.

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
Variables		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 diet quality scores
Diet quality scores	GWG	Model1^a			Model2^b			Model3^c
Diet quality scores	GWG	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)
HEI N = 280	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)
A-HEI N = 280	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)
DII N = 667	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)
DASH N = 667	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)
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 diet quality scores
Diet quality scores	Model1^a			Model2^b			Model3^c
Diet quality scores	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)
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 in-sufficient 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
Advanced maternal age*HEI	280	Excessive	-0.446(-0.673, -0.217)	< 0.001
Advanced maternal age*A-HEI	280	Insufficient	-0.307(-0.555, -0.057)	0.017
Advanced maternal age*A-HEI	280	Excessive	-0.288(-0.514, -0.062)	0.014
Advanced maternal age*DII	667	Insufficient	0.008(-0.832, 0.847)	0.986
Advanced maternal age*DII	667	Excessive	-0.316(-0.911, 0.281)	0.296
Advanced maternal age*DASH	667	Insufficient	0.057(-0.681, 0.794)	0.878
Advanced maternal age*DASH	667	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 GDM 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	138	-0.046(-0.248, 0.157)	0.644
Insufficient*A-HEI	138	-0.006(-0.202, 0.191)	0.949
Excessive*A-HEI	138	-0.282(-0.726, 0.160)	0.199
Insufficient*DII	333	0.034(-0.860, 0.928)	0.939
Excessive*DII	333	-0.270(-1.056, 0.513)	0.491
Insufficient*DASH	333	-2.263(-3.912, -0.625)	0.008
Excessive*DASH	333	-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.

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 ad-verse pregnancy outcomes was higher in advanced maternal age women[30]. The as-sociation 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 re-ported 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.

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.

Informed Consent

Statement: 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.

Institutional Review Board Statement

The protocols of NHANES were approved by the institutional review board of the National Center for Health Statistics, CDC. ID: NCHS IRB/ERB Protocol #98-12、Protocol #2005-06、Protocol #2011-17

Conflicts of Interest:

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Funding:

None.

Author Contributions:

YL and JH made the study design; YL, YC and JH conducted the study; YL and YiziM analyzed the data and wrote the manuscript; YL, YanxingM, CC, BQ, and JH participated amending the manuscript. All authors agreed with the final version of the manuscript.

Acknowledgments:

None.

Data Availability Statement:

Data described in the manuscript, code book, and analytic code will be made publicly and freely available without restriction at [ https://www.cdc.gov/nchs/nhanes ].

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The association of diet quality scores with the adverse pregnancy outcomes

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