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. 2021 Sep 13;11:18100. doi: 10.1038/s41598-021-97635-3

The effect of prepregnancy body mass index on maternal micronutrient status: a meta-analysis

Yan Yang 1,#, Zixin Cai 1,#, Jingjing Zhang 1,
PMCID: PMC8437962  PMID: 34518612

Abstract

The relationship between prepregnancy body mass index (BMI) and maternal micronutrient status is inconsistent and has not received sufficient attention. This meta-analysis aimed to evaluate the effect of prepregnancy BMI on micronutrient levels in pregnant women. PubMed, Embase, Web of Science, and the Cochrane Library were searched for articles that contained information on micronutrient levels and prepregnancy BMI. A random-effects model was used to determine the association between prepregnancy BMI and maternal micronutrient status. Sixty-one eligible articles were eventually included, with 83,554 participants. Vitamin B12, folate, vitamin D, iron and ferritin were the main micronutrients evaluated in our meta-analysis. Prepregnancy obesity and overweight may lead to an increased risk of micronutrient deficiency, including vitamin B12, folate and vitamin D deficiency, while prepregnancy obesity or overweight may have no significant association with ferritin deficiency. Additionally, the results of the dose–response analyses demonstrated a possible significant inverse correlation between prepregnancy BMI and levels of micronutrient, except for iron and ferritin. Compared with women with normal weight, women who were overweight or obese prepregnancy have lower micronutrient concentrations and are more likely to exhibit micronutrient deficiency during pregnancy, which is harmful to both mothers and neonates.

Subject terms: Physiology, Diseases, Endocrinology, Health care

Introduction

Maternal micronutrients play an important role in the health of both mothers and infants1,2. For children, maternal micronutrient deficiency can result in perinatal morbidity and mortality and can even lead to chronic complications, such as metabolic syndrome, in adult life1,3. For mothers, lean birth can lead to an increased risk of pregnancy complications, including gestational diabetes mellitus and preeclampsia2,4.

Maternal obesity, defined as a body mass index (BMI) greater than 30 kg/m25, is a major public health concern with an increasing prevalence worldwide6. Prepregnancy obesity has significant adverse effects on both mothers and offspring7. Obese women are more prone to experiencing stillbirth8, birth trauma7, gestational diabetes mellitus9 and preeclampsia10 than lean women. Additionally, adverse outcomes (e.g., preterm birth and congenital anomalies) are more common in infants of obese mothers11,12.

The micronutrient levels in the obese population are commonly ignored, particularly in pregnant women13. However, the consequences of maternal micronutrient deficiency are very harmful. Some of these adverse complications of obesity, such as preterm birth and congenital anomalies, have also been suggested to be related to maternal micronutrient status11,14. A report has demonstrated that vitamin D deficiency is common in obese women and increases the risk of food allergies15 and adiposity16 in offspring. Iron and ferritin may also be related to anthropometric results, while the exact connection is unknown. Increasing evidence has revealed a negative relationship between prepregnancy BMI and maternal micronutrition, mainly including vitamin B12, folate, vitamin D, iron and ferritin1720; other studies have shown the opposite results2124. Overall, the association between maternal micronutrition and obesity is unclear and remains to be studied. Given the inconsistent and ambiguous relationship between micronutrient levels and obesity in pregnant women, we conducted this meta-analysis to determine whether a higher prepregnancy BMI in mothers would lead to low micronutrient levels.

Results

Study characteristics

In total, 4319 studies were initially identified from 4 databases, including PubMed, the Web of Science, Embase and the Cochrane Library (Fig. 1). After removing duplicates, 1000 remaining studies were screened according to the titles and abstracts, and 460 studies were further excluded. Subsequently, 61 studies were selected after removing 487 studies according to the full-text screening. Finally, 61 articles14,2282 were included in our meta-analysis. The main characteristics of the 61 included articles are shown in Table 1. Most of these articles were published between 2010 and 2020. Additionally, the definitions of micronutrient deficiency and methods to measure micronutrient status are listed in Table 2.

Figure 1.

Figure 1

Flow diagram of the study selection process.

Table 1.

Characteristics of the included studies.

No Study Year Country Type Age Measurement of BMI Timing of micronutrient measurement Timing of BMI measurement n Type of micronutrient NOS
1 Adaikalakoteswari25 2015 UK Cross-sectional 32.7 ± 5.9 Maternal recall At 39–40 weeks of gestation At the first pregnancy visit 91 Vitamin B12 7
2 Shuying LI26 2019 China Cross-sectional 29.4 ± 4.5 NA at 24–28 weeks of gestation NA 406 Vitamin B12, folate 8
3 Riaz23 2018 Pakistan Prospective 24.78 ± 4.89 Measured  ≤ 13 weeks of gestation Before pregnancy 301 Vitamin B12, folate, iron and vitamin D 6
4 Jun S. Lai27 2017 Singapore Cross-sectional NA NA At 26–28 weeks of gestation Before pregnancy 913 Vitamin B12, folate 7
5 Peppard28 2019 USA Cross-sectional 27 Measured NA Before pregnancy 174 Vitamin B12 8
6 Scholing29 2018 Netherlands Cohort 30.9 ± 4.9 Maternal recall At 12–15 weeks of gestation At the first pregnancy visit 4243 Vitamin B12, folate, iron and ferritin 9
7 Monsen22 2016 Norway Cohort NA Maternal recall At 18 weeks of gestation At the first pregnancy visit 2797 Vitamin B12, folate 8
8 Bhowmik14 2019 Bangladesh Prospective 20.0 ± 2.6 Maternal recall At 6–14 weeks of gestation At the first pregnancy visit 498 Iron, ferritin, folate and B12 6
9 Shukri30 2015 UK Case–control NA NA At 16 and 28 weeks of gestation Before pregnancy 241 Vitamin B12, folate and iron 6
10 Berglund31 2016 Spain Cohort Normal-weight = 30.9 ± 4.2, Overweight = 32.0 ± 4.2, Obese = 29.5 ± 7.8 NA At 24 weeks of gestation Before pregnancy 331 Vitamin B12, folate and ferritin 8
11 YS Han32 2011 Korea Cross-sectional Underweight = 30.7 ± 3.6, Normal-weight = 32.3 ± 4.0, Overweight = 32.8 ± 3.7, Obese = 32.9 ± 3.8 Maternal recall At 19–39 weeks of gestation At the first pregnancy visit 608 Folate 8
12 Minxue Shen33 2016 Canada Cross-sectional NA Measured At 12–20 weeks of gestation NA 869 Folate 6
13 Tomedi34 2013 USA Cohort 30.3 ± 5.6 MATERNAL recall  ≤ 20 weeks of gestation At the first pregnancy visit 129 Folate, vitamin D 8
14 Yamada35 2012 Japan Cohort 38.5 ± 2.9 NA At 5–13 weeks of gestation Before pregnancy 5075 Folate 7
15 Santacruz36 2010 Spain Cohort Normal-weight = 31, Overweight = 29 Maternal recall At 24 weeks of gestation At the first pregnancy visit 50 Folate, iron 8
16 Shin37 2016 USA Cross-sectional NA Maternal recall NA At the first pregnancy visit 795 Folate, iron 8
17 Abbas38 2017 Sudan Cross-sectional 26.8 ± 6.2 Measured  < 14 weeks of gestation NA 423 Iron 6
18 Chang Cao24 2015 USA Cross-sectional 17.2 ± 1.1 Measured At mid-gestation and/or at delivery NA 230 Iron 9
19 Xiaobing Liu39 2017 China Cross-sectional 27.0 ± 4.5 Measured All trimesters Before pregnancy 1400 iron 7
20 Raguž40 2016 Bosnia and Herzegovina Cohort 29 NA At delivery Before pregnancy 128 Iron, ferritin 6
21 Lewandowska41 2020 Poland Cohort 34.8 ± 4.4 Maternal recall At 10–14 weeks of gestation At the first pregnancy visit 563 Iron 9
22 Quijano42 2019 Mexico Cohort Adequate Weight = 22.71 ± 1.95, Obese = 34.81 ± 4.80 Maternal recall At 13, 20, 27, and 34 weeks of gestation At the first pregnancy visit 93 Iron, ferritin 9
23 Koenig43 2020 USA Cross-sectional 27.6 ± 6.8 Maternal recall At 29–33 weeks of gestation At the first pregnancy visit 55 Iron, ferritin 8
24 Jones44 2016 China Longitudinal study Underweight = 24 ± 3.0, Normal-weight = 25 ± 3.5, Overweight = 26 ± 4.3, Obese = 25 ± 3.6 Maternal recall At 24–28 weeks of gestation At the first pregnancy visit 1613 Iron 7
25 Flynn45 2018 UK Cohort 30 ± 4.2 NA At 15–18 weeks of gestation At the first pregnancy visit 490 Ferritin 7
26 Espı´nola46 2018 Spain Cohort Normal-weight = 31 ± 7, Overweight = 33 ± 4, Obese = 30.50 ± 8 NA At 24–34 weeks of gestation Before pregnancy 157 Iron 9
27 Lewicka47 2019 Poland Cross-sectional 29.5 ± 4.8 Maternal recall At delivery At the first pregnancy visit 225 Iron 8
28 Mireku48 2016 Benin Cohort NA NA At the second trimester Before pregnancy 636 Iron 7
29 Bodnar49 2004 USA Cross-sectional NA Maternal recall At 24–29 weeks of gestation At the first pregnancy visit 439 Iron 6
30 Bener50 2013 Qatar Cohort NA NA Above 24 weeks of gestation Before pregnancy 1873 Iron, vitamin D 6
31 COSTA51 2016 Cohort 31 NA At 20 weeks of gestation Before pregnancy Iron 6
32 Figueiredo52 2019 Brazil Cohort 26 Maternal recall All trimesters At the first pregnancy visit 163 Vitamin D 8
33 Nobles53 2015 USA Cohort 18–40 At 15.2 weeks of gestation Before pregnancy 237 Vitamin D 9
34 Yun54 2015 China Cross-sectional 26.1 Maternal recall NA At the first pregnancy visit 1985 Vitamin D 7
35 Wang55 2019 China Cross-sectional Non-overweight and non-obesity = 28.8 ± 3.1, Overweight and obesity = 28.7 ± 3.2 Maternal recall At 24–28 weeks of gestation At the first pregnancy visit 140 Vitamin D 8
36 Chun56 2017 Korea Cross-sectional 31.6 NA At 3–17 weeks of gestation Before pregnancy 356 Vitamin D 8
37 Yan Tian57 2016 USA Cohort NA Maternal recall At 4–29 weeks of gestation At the first pregnancy visit 2558 Vitamin D 7
38 JM Thorp58 2012 USA Case–control Cases = 26.8 ± 5.5, Controls = 27.3 ± 5.6 Measured At 16–21 weeks of gestation Before pregnancy 265 Vitamin D 8
39 McAree59 2014 UK Retrospective NA Measured All trimesters Before pregnancy 346 Vitamin D 6
40 Sen60 2017 USA A secondary analysis of randomized controlled trial 28.4 ± 5.9 Measured At 16 and 28 weeks of gestation Before pregnancy 234 Vitamin D 7
41 Xin Zhao61 2017 China Cohort 27.3 ± 3.9 Maternal recall At 13 weeks of gestation At the first pregnancy visit 13,806 Vitamin D 9
42 Rodriguez62 2016 Spain Cohort 30.4 ± 4.3 Maternal recall At 12 weeks of gestation At the first pregnancy visit 2036 Vitamin D 9
43 Woon63 2019 Malaysia Cohort 29.9 ± 4.1 Measured Above 28 weeks of gestation Before pregnancy 535 Vitamin D 7
44 Tuck64 2015 Australia Cross-sectional 30.0 ± 5.4 Measured At 12 weeks of gestation Before pregnancy 1550 Vitamin D 7
45 Thiele65 2019 Portland Cohort 30.6 ± 4.46 NA Early pregnancy At the first pregnancy visit 357 Vitamin D 8
46 Leffelaar66 2010 Netherlands Cohort ≤ 24, 25–34, ≥ 35 Early pregnancy At the first pregnancy visit 3730 Vitamin D 9
47 Choi67 2015 Korea Cohort 32 Maternal recall All trimesters At the first pregnancy visit 220 Vitamin D 9
48 Eva Morales68 2014 Spain Cohort 30.2 ± 4.6, 30.4 ± 4.3, 31.0 ± 4.2 Maternal recall At 13–15 weeks of gestation At the first pregnancy visit 2358 Vitamin D 8
49 Santos69 2018 Brazil Cross-sectional 18–45 NA Second or third trimester At the first pregnancy visit 190 Vitamin D 9
50 Merewood70 2011 USA Cross-sectional < 20, 20–< 30, 30–43 Measured Second or third trimester Before pregnancy 459 Vitamin D 8
51 Karlsson71 2014 Sweden Cross-sectional Normal-weight = 31.4 ± 4.0 Obese = 32.0 ± 3.2 Maternal recall First trimester At the first pregnancy visit 105 Vitamin D 6
52 Burris72 2014 USA Cohort 32.1 ± 5.0 NA At 16.4–36.9 weeks of gestation Before pregnancy 1591 Vitamin D 8
53 Huang73 2014 USA Cohort 33.4 ± 4.2 Maternal recall First trimester At the first pregnancy visit 498 Vitamin D 9
54 Alonso74 2011 Spain Cross-sectional < 20, 20–29, ≥ 30 NA First trimester Before pregnancy 488 Vitamin D 6
55 Francis75 2018 USA Cohort 28.2 ± 0.5 Maternal recall At 10–14 and 15–26 weeks of gestation At the first pregnancy visit 321 Vitamin D 8
56 Johns76 2017 USA Cohort 18–24, 25–29, 30–34, ≥ 35 Measured At 22.9–36.2 weeks of gestation Before pregnancy 477 Vitamin D 6
57 Fernandez77 2014 USA Cohort 15–24, 25–34, ≥ 35 Maternal recall < 29 weeks of gestation At the first pregnancy visit 2583 Vitamin D 8
58 López78 2013 Spain Cross-sectional < 20, 20–29, ≥ 30 NA First trimester Before pregnancy 502 Vitamin D 6
59 Woolcott79 2016 Canada Case–control < 25, 25–< 30, 30–< 35, ≥ 35 NA At 20–28 weeks of gestation Before pregnancy 1635 Vitamin D 8
60 Jani80 2020 Australia Cohort 31.06 ± 5.176 Maternal recall At 14 weeks of gestation At the first pregnancy visit 16,528 Vitamin D 9
61 Daraki81 2018 Greece Cohort 29.7 ± 4.9 NA At 14 weeks of gestation Before pregnancy 1226 Vitamin D 8

NA data not available, NOS Newcastle–Ottawa Scale.

Table 2.

Characteristics of studies on micronutrient deficiency.

Study Methods of micronutrient measurement Definition of micronutrient deficiency
Musarrat Riaz (2018)23 ELISA/chemiluminescent immunoassay Vitamin D deficiency (< 30 ng/ml) and low vitamin B12 (< 190 ng/l)
Bhowmik (2019)14 ELISA/chemiluminescent immunoassay Vitamin D deficiency (< 30 nmol/l), vitamin B12 deficiency (< 200 pg/ml); folate deficiency (< 3 ng/ml) and iron deficiency (ferritin < 13 ng/ml)
Scholing (2018)29 Chemiluminescent immunoassay Folate deficiency (< 10·0 nmol/l), iron deficiency (ferritin < 15·0 μg/l) and vitamin B12 deficiency (< 203·3 pg/ml)
Monsen (2016)22 Microbiological assay NA
Abbas (2017)38 Radioimmunoassay gamma counter and kits Iron deficiency (ferritin < 15 μg/l)
Chang Cao (2015)24 ELISA Iron deficiency (ferritin < 12 μg/l)
Jones (2016)76 Chemiluminescent immunoassay Iron deficiency (ferritin < 15 μg/l)
Koenig (2020)43 NA Iron deficiency (ferritin < 12 μg/l)
Flynn (2018)45 ELISA Iron deficiency (ferritin < 15 μg/l)
Bodnar (2004)49 NA Iron deficiency (ferritin < 20 μg/l)
Nobles (2015)53 Heartland assays 25(OH)D < 20 ng/ml
Tomedi (2014)83 ELISA/chemiluminescent immunoassay NA
Rodriguez (2016)62 BioRAD kit 25(OH)D < 20 ng/ml
Lo´pez (2011)78 Chemiluminescent immunoassay 25(OH)D < 20 ng/ml
Morales (2014)68 Chemiluminescent immunoassay 25(OH)D < 20 ng/ml
Thiele (2019)65 NA 25(OH)D < 29 ng/ml
Leffelaar (2010)66 ELISA 25(OH)D < 29.9 ng/ml
Daraki (2018)81 Chemiluminescent immunoassay 25(OH)D < 37.7 nmol/l
Choi (2015)67 NA 25(OH)D < 20 ng/ml
Santos (2017)84 Chemiluminescent immunoassay 25(OH)D < 50 nmol/l
Merewood (2010)70 Competitive protein-binding assay 25(OH)D < 20 ng/ml
McAree (2013)59 Liquid chromatography coupled to tandem mass spectrometry 25(OH)D < 25 nmol/l
Jani (2020)80 NA 25(OH)D < 50 nmol/l
TUCK (2015)64 Chemiluminescent immunoassay 25(OH)D < 50 nmol/l
Woolcott (2016)79 chemiluminescent immunoassay 25(OH)D < 50 nmol/l

NA data not available.

Prepregnancy obesity (BMI ≥ 30) and micronutrient deficiency

The pooled results from three included studies suggested that prepregnancy obesity (BMI > 30) contributed to an increased risk of vitamin B12 deficiency (OR: 2.13; 95% CI 1.73, 2.64) (Fig. 2A). Additionally, the overall data from three eligible studies showed that, compared with normal weight, prepregnancy obesity was positively associated with the prevalence of folate deficiency during pregnancy (OR: 1.69; 95% CI 1.32, 2.16) (Fig. 2B). The results in Fig. 2C from 17 studies demonstrate that prepregnancy obesity may be positively associated with the prevalence of vitamin D deficiency (OR: 2.03; 95% CI 1.74, 2.37). However, the data extracted from seven studies revealed that prepregnancy obesity may not be significantly associated with the risk of ferritin deficiency during pregnancy (OR: 1.17; 95% CI 0.79, 1.73) (Fig. 2D).

Figure 2.

Figure 2

Forest plots of the relationship between prepregnancy obesity and micronutrient deficiency, including that of vitamin B12 (A), folate (B), vitamin D (C), and ferritin (D).

Prepregnancy overweight (BMI: 25–29.9) and micronutrient deficiency

The pooled result from four included studies suggested that prepregnancy overweight contributed to an increased risk of vitamin B12 deficiency (OR: 1.25; 95% CI 1.01, 1.54) (Fig. 3A). The overall data extracted from nine eligible studies showed that, compared with normal weight, prepregnancy overweight was positively associated with the prevalence of folate deficiency during pregnancy (OR: 1.57; 95% CI 1.05, 2.34) (Fig. 3B). The overall data showed that, compared with normal weight, prepregnancy overweight was positively associated with the prevalence of vitamin D deficiency during pregnancy (OR: 1.42; 95% CI 1.25, 1.60) (Fig. 3C). Additionally, prepregnancy overweight may not be significantly associated with the risk of ferritin deficiency (OR: 0.85; 95% CI 0.63, 1.16) (Fig. 3D).

Figure 3.

Figure 3

Forest plots of the relationship between prepregnancy overweight and micronutrient deficiency, including that of vitamin B12 (A), folate (B), vitamin D (C), and iron (D).

Prepregnancy BMI and micronutrient level

To further examine the relationship between prepregnancy BMI and vitamin B12, subgroup analysis based on prepregnancy BMI categories was conducted (Fig. 4A). The greatest decreases in vitamin B12 levels were observed in obese women (WMD: − 61.90 pg/ml; 95% CI [− 69.47, − 54.32]), followed by the overweight group (WMD: − 30.53 pg/ml; 95% CI [− 35.97, − 25.08]). However, prepregnancy underweight was not associated with maternal vitamin B12 levels (WMD: 5.9 pg/ml; 95% CI [− 5.45, 16.03]).

Figure 4.

Figure 4

Figure 4

Figure 4

Forest plots between prepregnancy BMI and micronutrient deficiency, including vitamin B12 (A), folate (B), vitamin D (C), iron (D) and ferritin (E). Subgroup analysis of combined weighted mean differences with 95% confidence intervals was stratified by the prepregnancy BMI.

Second, subgroup analysis of the folate levels based on the prepregnancy BMI categories is shown in Fig. 4B. The greatest decreases in folate levels were observed in overweight women (WMD: − 1.52 ng/ml; 95% CI [− 1.69, − 1.36]) and the obese group (WMD: − 1.54 ng/ml; 95% CI [− 1.63, − 1.46]), while underweight prepregnancy may increase maternal folate levels (WMD: 2.05 ng/ml; 95% CI [1.82, 2.27]).

Third, the association of different prepregnancy BMI categories and vitamin D levels is revealed in Fig. 4C. Maternal vitamin D levels were significantly reduced in prepregnancy obese women (WMD: − 5.66 ng/ml; 95% CI [− 5.77, − 5.55]) and the overweight group (WMD: − 1.98 ng/ml; 95% CI [− 2.08, − 1.89]), while underweight prepregnancy may slightly increase maternal vitamin D levels (WMD: 0.20 ng/ml; 95% CI [0.007, 0.32]).

Additionally, the results of the association between different prepregnancy BMI categories and maternal iron were consistent (Fig. 4D). Compared with the normal-weight group, abnormal prepregnancy BMI decreased maternal iron levels (underweight WMD: − 118 µg/L; 95% CI [− 136.74, − 99.27]; overweight WMD: − 181.05 µg/L; 95% CI [− 187.79, − 174.30]; obese WMD: − 194.11 µg/L; 95% CI [− 203.44, − 184.78]).

However, as high heterogeneity existed in the above results (Fig. 4), we further conducted subgroup analysis based on methods for BMI measurement, timing of micronutrient measurement and timing of BMI measurement in underweight, overweight and obese women (Supplementary Tables 13). Although heterogeneity showed a certain degree of decline or increase, no true cause of heterogeneity can be fully identified, which may result from other information not provided in the included studies.

In contrast to iron, the association between prepregnancy BMI and serum ferritin was inconsistent. Prepregnancy underweight and obesity may be slightly related to the maternal ferritin level (underweight WMD: 4.07 µg/l, 95% CI [2.45, 5.66]; obese WMD: 7.36 µg/l, 95% CI [6.41, 8.36]), while overweight was not associated with ferritin level during pregnancy (WMD: − 0.04 ng/ml; 95% CI [− 0.68, 0.60]) (Fig. 4E).

Dose–response analysis of prepregnancy BMI and micronutrients

Ten studies related to vitamin B12 were included; among them, 24 results were used to examine the dose–response relationship between prepregnancy BMI and vitamin B12. An inverse correlation was observed, as shown in Fig. 5A (coefficient =  − 55.12; P = 0.001).

Figure 5.

Figure 5

Nonlinear dose responses between prepregnancy BMI and micronutrient levels, including those of vitamin B12 (A), folate (B), vitamin D (C), iron (D) and ferritin (E).

Thirty-nine data points extracted from 15 studies demonstrated a significant inverse association between prepregnancy BMI and maternal folate (coefficient =  − 1.37; P < 0.001) (Fig. 5B).

The level of vitamin D was assessed by 25(OH) D measurement in the included articles to examine the association between prepregnancy BMI and vitamin D. Twenty-one studies were included in this analysis, and 45 results were extracted from the 21 studies. However, a significant inverse association was found between prepregnancy BMI and serum vitamin D (coefficient =  − 4.14; P < 0.001) (Fig. 5C).

Eleven studies and 20 subsequent data points revealed a significant inverse relationship between prepregnancy BMI and serum iron (coefficient =  − 165.12; P = 0.001) (Fig. 5D).

Fourteen studies were included, and 30 data points were extracted to examine the association between prepregnancy BMI and serum ferritin. No significant relationship was observed between prepregnancy BMI and serum ferritin (coefficient =  − 0.944; P = 0.682) (Fig. 5E).

Evaluation of publication bias and sensitivity analysis

Funnel plots, Egger’s regression test and Begg’s rank correlation test were used to analyse publication bias in our meta-analysis. The proportion of statistically significant publication bias tests was not observed for larger meta-analyses, as detected by either Begg’s or Egger’s test (P > 0.05). Funnel plots also showed symmetric distribution in every analysis (Fig. 6). Overall, no publication bias was found in our meta-analysis. Additionally, sensitivity analysis further demonstrated that our results were stable (Fig. 7).

Figure 6.

Figure 6

Funnel plots between prepregnancy obesity and micronutrient deficiency, including those of vitamin B12 (A), folate (B), vitamin D (C), and iron (D).

Figure 7.

Figure 7

Sensitivity analysis between prepregnancy obesity and micronutrient deficiency, including that of vitamin B12 (A), folate (B), vitamin D (C), and iron (D).

Discussion

Micronutrients play an important role in the health of mothers and offspring. The levels of micronutrients in the obese population, particularly in obese pregnant women, are usually neglected. However, recent studies have shown that an inverse relationship may exist between obesity and micronutrient levels17,85, while some studies have found the opposite relationship23,24. Therefore, we performed the present meta-analysis to resolve this discrepancy. To the best of our knowledge, this systematic review and meta-analysis is the first to assess the relationship between prepregnancy BMI and pregnancy micronutrient levels.

Our study mainly focused on five common micronutrients: vitamin B12, folate, vitamin D, iron and ferritin. Based on our findings from all 62 papers, micronutrient deficiencies, including those of vitamin B12, folate, and vitamin D, were more frequent in obese or overweight pregnant women than in nonobese women (Figs. 2 and 3). Additionally, we found a direct inverse association in pregnant women between prepregnancy BMI and maternal levels of micronutrients, except for ferritin (Figs. 4 and 5).

The aetiology of the inverse relationship between prepregnancy BMI and pregnancy micronutrient levels is unknown. Several factors may partially explain the link between BMI and maternal micronutrition. First, the consumption of a low-quality diet, characterized by less fruit and more calories, including solid fats, alcohol and added sugar37, may be an underlying mechanism. Obese people are more likely to consume a low-quality diet, which contributes to a lower intake of micronutrients before and during pregnancy than that of normal-weight women37,86.

Second, hepcidin, a marker of chronic inflammation in obesity87, may play a significant role in the association between prepregnancy BMI and iron. As an iron-regulating hormone88,89, hepcidin is increased in obese women, leading to reduced iron absorption and release87. Therefore, prepregnancy BMI may lead to a reduced level of iron in serum by inhibiting iron absorption.

Additionally, the lipid profile, a marker of obesity, is inversely associated with the level of vitamin B12 in T2DM patients90. Additionally, blood pressure and metabolic syndrome, complications of obesity, were accompanied by a low vitamin B12 status91,92. Thus, vitamin B12 may be reduced because of lipid disorders or complications of obesity.

Our meta-analysis has both practical and research implications. Regarding practical implications, we found that obese prepregnant women have a greater risk of micronutrient deficiency during pregnancy, indicating the importance of micronutrient supplementation and supervision in obese pregnant women. Additionally, we performed dose–response analyses to demonstrate the relationship between prepregnancy BMI and maternal micronutrient levels, including those of vitamin B12, folate, vitamin D, iron and ferritin. Finally, the relationship between prepregnancy obesity and micronutrients was systematically summarized in our study. Regarding research implications, identifying the underlying mechanisms of the effects of prepregnancy BMI on micronutrient deficiency may be an important direction of future research in this field to keep mothers and infants safe.

Although our study partially revealed the effects of obesity on pregnancy micronutrient levels, these levels were only measured during pregnancy and not before pregnancy in the included articles. Hence, future studies should include more details, such as prepregnancy micronutrient levels, to fully prove causality between BMI and pregnancy micronutrient levels. Additionally, high heterogeneity existed in our results. Information on the method and timing of BMI measurements, period of micronutrient measurement (Table 1) and definition of micronutrient deficiency (Table 2) were inconsistent, likely contributing to the high heterogeneity of our results. Furthermore, because some prepregnancy BMIs were obtained from maternal recall, which is not as accurate as the measured BMIs (Table 1), recall bias may exist in our analysis, and future clinical studies should focus more on the use of uniformly measured prepregnancy BMIs to avoid this bias. Moreover, the definition of micronutrient deficiency was not uniform in the different included papers (Table 2); for example, the different standards of deficiency are also a limitation, and more well-designed clinical studies are required. Additionally, as we did not add other iron biomarkers, including transferrin receptor and transferrin saturation, future meta-analyses to analyse the association between prepregnancy BMI and other iron levels are needed.

Finally, because micronutrient concentrations are often measured from plasma or serum, rather than whole blood, plasma volume changes during pregnancy can influence the concentrations of these micronutrients93,94. Therefore, new micronutrient cut-offs may be needed in future studies to avoid the possible effect of haemodilution in pregnant women. However, we focused on the relationship between prepregnancy BMI and maternal micronutrient levels, and the target population was pregnant women; thus, the effect of haemodilution may not affect our conclusion.

In conclusion, our study revealed that prepregnancy obesity or overweight may lead to an increased risk of micronutrient deficiency during pregnancy. Therefore, we emphasize that clinical micronutrient screening is necessary for overweight or obese pregnant women.

Methods

Search strategy

This meta-analysis was rigorously reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, as previously described95. This protocol analysis was registered on the PROSPERO website (protocol number: CRD42020188646). In this study, four electronic databases, PubMed, Embase, Cochrane Library and Web of Science, were searched for articles relevant to micronutrients and obesity through May 2020. The search terms were “BMI”, “obesity”, “overweight” and “body mass index” combined with “micronutrient”, “vitamin B12”, “folate”, “vitamin D”, “iron”, and “ferritin”. Additionally, we evaluated the references of the articles and reviews on micronutrients to identify studies that were not indexed in the databases but would be eligible for inclusion in this meta-analysis.

Selection criteria

Two reviewers (YY and ZC) reviewed all the included studies and determined study eligibility. Disagreements were settled by consensus or the help of a third reviewer (JZ). All the articles included in this meta-analysis met the following criteria: (1) studies with information on obesity and micronutrients; (2) studies published in English; and (3) studies in which the micronutrients were limited to vitamin B12, folate, vitamin D, iron and ferritin. Additionally, articles were excluded if they met the following criteria: (1) articles that involved individuals who had undergone bariatric surgery; (2) articles that were literature reviews, communications or editorials; (3) studies with methodological weaknesses, such as inference data for the population from a nonrepresentative sample and studies that evaluated the relationship between prepregnancy BMI and nutritional status but did not explain the methodology or parameters used to evaluate these events; (4) studies in which data reported only in meeting abstracts would have been included if the abstract contained sufficient information for assessment; and (5) studies that did not have available information or usable data for this meta-analysis.

Data extraction

All relevant articles were entered in EndNote X8 software and reviewed independently by two authors (YY and ZC). Discrepancies between authors were settled with the help of a third reviewer (JZ). The following information was extracted from the final studies: name of the first author, year of publication, country, sample size, study design, prepregnancy BMI, type of micronutrient, level of micronutrient, and odds ratio (OR) and 95% confidence interval (CI) of the micronutrient deficiency. All the extracted data were then imported into Excel software.

Quality assessment of studies

The quality of the included studies was assessed using the Newcastle–Ottawa Scale (NOS)96. The measures on this scale comprise three items: the selection of participants, comparability of cases and controls, and ascertainment of outcomes. The scale has a minimum score of 0 and a maximum score of 9. Studies scoring at least 7 (corresponding to 78% of the maximum score) were regarded as having a low risk of bias (‘good’ quality), those scoring 4–6 were deemed to have a modest risk of bias (‘fair’ quality), and those scoring < 3 were considered to have a substantial risk of bias (‘poor’ quality)97. We assessed the quality of all the relevant studies in accordance with the type of study, sample size, participant selection, representativeness of the sample (case or exposure group), adequacy of follow-up, comparability (exposed-unexposed or case–control), and method of ascertainment for cases and controls. Finally, high-quality studies were included in the analyses. Two investigators (YY and ZC) independently performed the quality assessment. Any disagreements were settled with the help of a third reviewer (JZ) when necessary.

Definition

Based on all the included studies, we classified BMI based on the World Health Organization (WHO) standards (underweight: BMI ≤ 18.5; normal weight: BMI 18.5–24.9; overweight: BMI 25–29.9; obesity: BMI ≥ 30). Doses (mean of BMI category) were defined as follows according to the data from the Scott‐Pillai study98: BMI 18.5–24.9 = 21.7; BMI 25–29.9 = 27.45; BMI 30–34.9 = 32.45; BMI 35–39.9 = 37.45; BMI ≤ 20 = 18.5; BMI < 25 = 21; BMI ≥ 25 = 30; BMI < 30 = 23.7; BMI ≥ 30 = 34.6; BMI ≥ 35 = 38.5; BMI ≤ 18.5 = 18 and BMI ≥ 40 = 41. Additionally, ferritin is an iron-storing protein, with serum ferritin regarded as a measurement of total body iron stores99. Furthermore, independent of iron status, serum ferritin is also increased by inflammation in the body because ferritin is an acute-phase protein99. To evaluate the potential dose–response relationship between BMI and micronutrient levels, a dose–response meta-analysis was conducted to compute the trend from the correlated values of BMI across various micronutrient levels.

Statistical analysis

We gathered data on the prevalence of micronutrient deficiencies in various groups classified according to prepregnancy BMI. We gathered the results worldwide from different ethnicities and regions. Therefore, we used the random-effects model to obtain the meta-analysis results. Odds ratios (ORs) and CIs were used as summary measurements for the meta-analysis, and the results are presented as forest plots. Continuous variable effect size was defined as weighted mean differences (WMDs) and 95% CIs calculated for changes in micronutrient concentrations. Pooled WMDs with 95% CIs were calculated using the mean and standard deviation from each study by Stata 5 software. The correlation coefficient was used as another summary measure for the outcome studies, presented as dose response analyses. All statistical analyses were performed using Stata software (Version 13.0). The heterogeneity among all the studies was assessed by I2 statistics. The bias of the study was analysed using funnel plots. Sensitivity analysis was performed by leaving out each study one by one to evaluate the credibility of the pooled results.

Supplementary Information

Supplementary Tables. (31.5KB, docx)

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (82070807, 91749118, 81770775, 81730022), Natural Science Foundation of Hunan Province, China (2021JJ30976) and National key research and development program (2019YFA0801903, 2018YFC2000100).

Author contributions

J.Z. coordinated and planned the study. Y.Y. conceived the study, along with Z.C. and J.Z., and contributed to the study design, literature search, statistical analysis, and data synthesis of the outcomes. Y.Y. prepared the first draft of the manuscript with significant help from Z.C. and J.Z. All the authors critically revised the results to produce the final version.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Yan Yang and Zixin Cai.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-021-97635-3.

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