父亲膳食模式与辅助生殖子代出生体重的关联

朱芸芸; 杨东见; 冷姝芳; 李红

doi:10.16766/j.cnki.issn.1674-4152.004350

父亲膳食模式与辅助生殖子代出生体重的关联

doi: 10.16766/j.cnki.issn.1674-4152.004350

朱芸芸^{1, 2},
杨东见^{1, 3},
冷姝芳¹,
李红^{1, 3, ,}

1.
上海交通大学医学院附属国际和平妇幼保健院护理部，上海 200030
2.
上海交通大学护理学院，上海 200025
3.
上海市胚胎源性疾病重点实验室，上海 200030

基金项目:

国家重点研发计划课题 2022YFC2702903

上海交通大学医学院护理科研面上项目 Jyh2402

详细信息

通讯作者:
李红，E-mail：2750074682@qq.com

中图分类号: R169 R153.9
计量
- 文章访问数: 6
- HTML全文浏览量: 4
- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2025-01-21
- 网络出版日期: 2026-04-01

Association of paternal dietary patterns with birth weight of assisted reproduction offspring

ZHU Yunyun^{1, 2},
YANG Dongjian^{1, 3},
LENG Shufang¹,
LI Hong^{1, 3
, ,}

1.
Nursing Department, the International Peace Maternal and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China

摘要

摘要: 目的探索父亲膳食模式与辅助生殖(assisted reproductive technology, ART)子代出生体重之间的关联性，分析影响子代体重的相关膳食因素，为ART子代健康发育提供依据。方法选取2017年1月1日—2021年12月31日在中国国家出生队列辅助生殖技术子代胚胎源性疾病队列研究(China National Birth Cohort, CNBC)项目招募的904组父亲及ART子代为研究对象，使用半定量食物频率问卷收集膳食摄入情况，主成分分析法提取膳食模式，利用限制性立方函数及多因素logistic回归分析父亲膳食模式和ART子代出生体重的关联。结果通过主成分分析法归纳出“果蔬”模式、“高嘌呤”模式、“植物性主食”模式、“优质动物蛋白”模式以及“糖脂”模式5种膳食模式。按膳食模式得分四分位数分组，调整协变量后，与父亲处于“优质动物蛋白”模式Q1(最低)组相比，处于Q4组(最高)的ART子代发生小于胎龄儿(small for gestational age, SGA)的风险更低(OR=0.257，95% CI: 0.079~0.712)，且随分值增加SGA风险降低(P-trend=0.007)。结论父亲采取“优质动物蛋白”的膳食模式可能降低ART子代罹患SGA的风险。父亲应在备孕期间增加优质蛋白摄入，保障胎儿的正常生长发育，降低不良出生结局的风险。
- 父亲 /
- 膳食模式 /
- 辅助生殖 /
- 出生体重 /
- 子代
Abstract: Objective To explore the correlation between the father ' s dietary pattern and the birth weight of assisted reproductive technology (ART) offspring, and analyze the relevant dietary factors influencing the weight of offspring, and provide a basis for promoting the healthy development of ART offspring. Methods A total of 904 groups of fathers and ART offspring recruited in the China National Birth Cohort (CNBC) project from January 1, 2017 to December 31, 2021 were selected as the research subjects. The dietary intake was collected using the semi-quantitative food frequency questionnaire, the dietary patterns were extracted by principal component analysis, and the association between the father ' s dietary patterns and the birth weight of ART offspring was analyzed using the restricted cube function and multivariate logistic regression. Results Five dietary patterns were summarized through principal component analysis: the "fruit and vegetable" model, the "high purine" model, the "plant-based staple food" model, the "high-quality animal protein" model, and the "sugar-lipid" model. Grouped by quartiles of dietary pattern scores and after adjusting for covariates, compared with the group where the father was in the "high-quality animal protein" pattern Q1 (lowest), the ART offspring in the Q4 group (highest) had a lower risk of small for gestational age (SGA, OR=0.257, 95% CI: 0.079-0.712), and the SGA risk decreased with the increase of the score (P-trend=0.007). Conclusion A dietary pattern of "high-quality animal protein" adopted by fathers may reduce the risk of SGA in the offspring of ART. Fathers should increase their intake of high-quality protein during the preconception period to ensure the normal growth and development of the fetus and reduce the risk of adverse birth outcomes.
- Father /
- Dietary patterns /
- Assisted reproduction /
- Birth weight /
- Offspring

HTML全文

图 1 父亲膳食模式得分与胎龄别体质量Z评分的限制立方函数分析

注：果蔬模式(A)，高嘌呤模式(B)，糖脂模式(C)，植物性主食模式(D)，优质动物蛋白模式(E)。调整协变量为父亲年龄、受教育时间、BMI、吸烟、饮酒、运动、睡眠、家庭年收入、母亲年龄、孕前BMI、孕期二手烟暴露、子代性别、总能量。

Figure 1. Analysis of the restricted cube function of the father ' s dietary pattern score and the Z-score of gestational age and body weight

下载: 全尺寸图片幻灯片

表 1 按子代出生体重分组的父亲人口学及生活方式比较

Table 1. Comparison of father ' s demographics and lifestyle based on the birth weight of the offspring

项目	总数(n=904)	AGA(n=699)	SGA(n=35)	LGA(n=170)	统计量	P值
年龄(x±s, 岁)	33.93±4.62	33.94±4.66	35.23±3.99	33.64±4.54	1.723^a	0.179
BMI(x±s)	24.61±3.28	24.55±3.23	24.39±3.30	24.87±3.45	492.600^a	< 0.001
总能量(x±s, kcal)	1 791.11±616.95	1 784.35±612.58	1 703.57±583.73	1 836.96±641.46	0.736^a	0.391
蛋白质(x±s, g)	81.02±30.64	80.82±29.88	78.11±35.36	82.44±32.77	0.276^a	0.600
脂肪(x±s, g)	65.33±27.30	65.35±26.76	62.82±28.99	65.81±29.21	0.013^a	0.911
碳水化合物(x±s, g)	224.23±76.23	222.68±77.29	211.02±58.81	233.36±74.51	2.149^a	0.143
受教育时间[例(%)]					1.079^b	0.583
< 12年	75(8.30)	58(8.30)	4(11.43)	13(7.65)
12~16年	669(74.00)	514(73.53)	24(68.57)	131(77.06)
>16年	160(17.70)	127(18.17)	7(20.00)	26(15.29)
家庭年收入[例(%)]					3.109^b	0.211
< 20万元	358(39.60)	270(38.63)	17(48.57)	71(41.76)
20万元~30万元	250(27.65)	189(27.04)	11(31.43)	50(29.41)
>30万元	296(32.74)	240(34.33)	7(20.00)	49(28.82)
居住地区[例(%)]					55.300^b	< 0.001
城市	844(93.36)	652(93.28)	31(88.57)	161(94.71)
乡镇农村	60(6.64)	47(6.72)	4(11.43)	9(5.29)
吸烟[例(%)]					278.820^b	< 0.001
有	320(35.40)	243(34.76)	8(22.86)	69(40.59)
无	584(64.60)	456(65.24)	27(77.14)	101(59.41)
饮酒[例(%)]					138.580^b	< 0.001
有	165(18.25)	124(17.74)	5(14.29)	36(21.18)
无	739(81.75)	575(82.26)	30(85.71)	134(78.82)
运动[例(%)]					307.080^b	< 0.001
有	334(36.95)	258(36.91)	7(20.00)	69(40.59)
无	570(63.05)	441(63.09)	28(80.00)	101(59.41)
睡眠时长[例(%)]					261.610^b	< 0.001
< 8 h	636(70.35)	487(69.67)	27(77.14)	122(71.76)
≥8 h	268(29.65)	212(30.33)	8(22.86)	48(28.24)
注：^a为F值，^b为χ²值。

下载: 导出CSV

表 2 5种膳食模式的因子载荷

Table 2. Factor loads of 5 dietary patterns

食物组	膳食模式
食物组	果蔬	高嘌呤	植物性主食	优质动物蛋白	糖脂混合
水果	0.858	-	-	-	-
橘类	0.706	-	-	-	-
蔬菜	0.614	-	-	-	-
软体动物及贝壳类	-	0.797	-	-	-
动物内脏及血制品	-	0.629	-	-	-
鱼类及虾类	-	0.576	-	-	-
大豆及其制品	-	-	0.648	-	-
大米及面食	-	-	0.607	-	-
蘑菇及藻类	-	-	0.555	-	-
粗粮及薯类	-	-	0.520	-	-
肉类	-	-	-	0.670	-
牛奶	-	-	-	0.627	-
鸡蛋	-	-	-	0.625	-
烘焙甜食类	-	-	-	-	0.793
油炸及膨化食品	-	-	-	-	0.645
坚果	-	-	-	-	-
注：提取方法为主成分分析法，斜交旋转。保留因子载荷绝对值>0.5的因子。“-”表示该食物类别在膳食模式中的因子载荷绝对值≤0.5，其他因子载荷≤0.5的因子载荷需增加“-”。

下载: 导出CSV

表 3 自变量赋值情况

Table 3. Assignment of categorical variables

自变量	赋值方法
受教育时间	＜12年=1, 12~16年=2，＞16年=3
吸烟	无=0，有=1
饮酒	无=0，有=1
运动	无=0，有=1
睡眠	＜8 h=0，≥8 h=1
家庭年收入	< 20万元=1，20万元~30万元=2
孕期二手烟暴露	无=0，有=1
子代性别	男=1，女=2

下载: 导出CSV

表 4 父亲膳食模式评分与SGA的多因素logistic回归分析

Table 4. Multivariate logistic regression analysis of fathers ' dietary pattern scores and SGA

膳食模式	调整前			调整后
膳食模式	B	OR(95% CI)	P值	B	OR(95% CI)	P值
果蔬
Q2	-0.588	0.556(0.218~1.325)	0.195	-0.848	0.428(0.162~1.062)	0.074
Q3	-0.884	0.413(0.144~1.050)	0.075	-1.113	0.328(0.109~0.88)	0.034
Q4	-0.726	0.484(0.180~1.187)	0.125	-0.859	0.424(0.152~1.083)	0.082
P-trend		0.076				0.057
高嘌呤
Q2	0.232	1.262(0.488~3.362)	0.631	0.174	1.190(0.447~3.260)	0.727
Q3	0.000	1.000(0.362~2.764)	0.999	-0.022	0.978(0.340~2.802)	0.966
Q4	0.122	1.130(0.425~3.062)	0.805	0.241	1.273(0.461~3.577)	0.639
P-trend		0.939				0.750
糖脂混合
Q2	-0.816	0.442(0.137~1.237)	0.136	-0.827	0.437(0.133~1.253)	0.139
Q3	0.092	1.096(0.470~2.577)	0.831	0.040	1.041(0.436~2.514)	0.927
Q4	-0.470	0.625(0.226~1.616)	0.340	-0.422	0.656(0.232~1.737)	0.403
P-trend		0.700				0.751
植物性主食
Q2	-0.816	0.442(0.137~1.237)	0.136	-0.911	0.402(0.123~1.150)	0.103
Q3	-0.100	0.905(0.370~2.189)	0.823	-0.242	0.785(0.314~1.939)	0.598
Q4	-0.210	0.811(0.321~1.997)	0.648	-0.325	0.723(0.278~1.827)	0.493
P-trend		0.939				0.746
优质动物蛋白
Q2	-1.093	0.335(0.119~0.824)	0.024	-1.079	0.340(0.118~0.858)	0.030
Q3	-0.934	0.393(0.149~0.931)	0.042	-1.093	0.335(0.122~0.835)	0.024
Q4	-1.280	0.278(0.090~0.717)	0.013	-1.359	0.257(0.079~0.712)	0.014
P-trend		0.008				0.007
注：Q1、Q2、Q3、Q4为膳食模式得分升序后进行四等分的分类。各膳食模式均以Q1为参照。调整变量为父亲年龄、受教育时间、BMI、吸烟、饮酒、运动、睡眠、家庭年收入、母亲年龄、BMI、孕期二手烟暴露、子代性别、总能量。

下载: 导出CSV

表 5 父亲优质动物蛋白膳食模式评分与SGA的分层分析

Table 5. Stratified analysis of dietary pattern score of high quality animal protein in fathers and SGA

父亲特征	OR(95% CI)	P值	父亲特征	OR(95% CI)	P值
BMI < 24			BMI≥24
Q2	0.215(0.039~0.928)	0.051	Q2	0.340(0.071~1.234)	0.125
Q3	0.281(0.061~1.105)	0.079	Q3	0.310(0.060~1.233)	0.119
Q4	0.093(0.005~0.630)	0.038	Q4	0.358(0.084~1.297)	0.133
年龄 < 35岁			年龄≥35岁
Q2	0.182(0.026~0.765)	0.037	Q2	0.558(0.130~2.090)	0.399
Q3	0.199(0.040~0.755)	0.026	Q3	0.407(0.092~1.566)	0.204
Q4	0.086(0.004~0.529)	0.028	Q4	0.458(0.100~1.846)	0.283
睡眠 < 8 h			睡眠≥8 h
Q2	0.339(0.102~0.977)	0.056	Q2	0.458(0.019~4.324)	0.539
Q3	0.372(0.126~1.011)	0.059	Q3	-	0.993
Q4	0.104(0.015~0.426)	0.005	Q4	1.187(0.165~8.064)	0.858
运动			不运动
Q2	0.676(0.05~8.343)	0.752	Q2	0.297(0.080~0.883)	0.042
Q3	0.231(0.005~4.249)	0.358	Q3	0.348(0.111~0.971)	0.053
Q4	0.200(0.004~4.205)	0.338	Q4	0.237(0.061~0.749)	0.022
注：Q1、Q2、Q3、Q4为膳食模式得分升序后进行四等分的分类，均以Q1为参照。调整变量为父亲年龄、受教育时间、BMI、吸烟、饮酒、运动、睡眠、家庭年收入，母亲年龄、BMI、处于二手烟环境，子代性别、总能量。“-”表示该食物类别在膳食模式中的因子载荷绝对值≤0.5，所以不显示具体数值。

下载: 导出CSV

参考文献(20)

[1]	孔北华, 马丁, 段涛. 妇产科学[M]. 10版. 北京: 人民卫生出版社, 2024: 399. KONG B H, MA D, DUAN T. Obstetrics and gynecology[M]. 10th edition. Beijing: People's Medical Publishing House, 2024: 399.
[2]	李敏, 王宝珠. 陕西省2010-2020年5岁以下儿童死亡监测结果分析[J]. 中华疾病控制杂志, 2023, 27(9): 1019-1024. LI M, WANG B Z. Analysis of mortality surveillance results for children under five in Shaanxi province from 2010 to 2020[J]. Chinese Journal of Disease Control and Prevention, 2023, 27(9): 1019-1024.
[3]	ARDISSINO M, MORLEY A P, SLOB E A W, et al. Birth weight influences cardiac structure, function, and disease risk: evidence of a causal association[J]. Eur Heart J, 2024, 45(6): 443-454. doi: 10.1093/eurheartj/ehad631
[4]	TOMAR A, GOMEZ V M, GERLINI R, et al. Epigenetic inheritance of diet-induced and sperm-borne mitochondrial RNAs[J]. Nature, 2024, 630(8017): 720-727. doi: 10.1038/s41586-024-07472-3
[5]	郭姝, 潘娟, 李妍, 等. 育龄期女性孕前膳食模式与新生儿出生体重的关联性研究[J]. 华中科技大学学报(医学版), 2024, 53(1): 52-58. GUO S, PAN J, LI Y, et al. Study on Association of Preconception Dietary Patterns with Neonate Birth Weight[J]. Acta Medicinae Universitatis Scientiae et Technologiae Huazhong, 2024, 53(1): 52-58.
[6]	LIU D, CHENG Y, MI B, et al. Maternal dietary patterns during pregnancy derived by reduced-rank regression and birth weight in the Chinese population[J]. Br J Nut, 2020, 123(10): 1176-1186. doi: 10.1017/S0007114520000392
[7]	DING Y, YANG Y, LI F, et al. Development and validation of a photographic atlas of food portions for accurate quantification of dietary intakes in China[J]. J Hum Nutr Diet, 2021, 34(3): 604-615. doi: 10.1111/jhn.12844
[8]	VAN S N, CUI J, WANG Y, et al. Identifying first-trimester risk factors for SGA-LGA using weighted inheritance voting ensemble learning[J]. Bioengineering (Basel), 2024, 11(7): 657. DOI: 10.3390/bioengineering11070657.
[9]	MORGAN H L, FURSE S, DIAS I H K, et al. Paternal low protein diet perturbs inter-generational metabolic homeostasis in a tissue-specific manner in mice[J]. Commun Biol, 2022, 5(1): 929. DOI: 10.1038/s42003-022-03914-8.
[10]	MAI H. Association of diet and lifestyle factors with semen quality in male partners of Chinese couples preparing for pregnancy[J]. Reprod Health, 2023, 20(1): 173. DOI: 10.1186/s12978-023-01718-5.
[11]	ABDOLLAHI N, NOURI M, LEILAMI K, et al. The relationship between plant and animal based protein with semen parameters: a cross-sectional study in infertile men[J]. Clin Nutr ESPEN, 2022, 49: 372-377. doi: 10.1016/j.clnesp.2022.03.019
[12]	DIMOFSKI P, MEYRE D, DREUMONT N, et al. Consequences of paternal nutrition on offspring health and disease[J]. Nutrients, 2021, 13(8): 2818. DOI: 10.3390/nu13082818.
[13]	DE LAUZON G B, KRINITZKI S, LIORET S, et al. Paternal diet before conception and its social determinants in the Elbe cohort[J]. Nutrients, 2022, 14(19): 4008. DOI: 10.3390/nu14194008.
[14]	张雪云, 王婕妤, 王春艳, 等. 育龄期不孕症女性的膳食模式现状及其影响因素分析[J]. 中华全科医学, 2024, 22(2): 252-256. doi: 10.16766/j.cnki.issn.1674-4152.003377 ZHANG X Y, WANG J Y, WANG C Y, et al. Analysis of dietary patterns and influencing factors in infertile women of childbearing age[J]. Chinese Journal of General Practice, 2024, 22(2): 252-256. doi: 10.16766/j.cnki.issn.1674-4152.003377
[15]	ZHANG Y, ZHAO Y, DUAN Y, et al. Effects of prepregnancy dietary patterns on infant birth weight: a prospective cohort study[J]. J Matern Fetal Neonatal Med, 2023, 36(2): 2273216. DOI: 10.1080/14767058.2023.2273216.
[16]	YAN H, DANG S, ZHANG Y, et al. Dietary patterns of Chinese women of childbearing age during pregnancy and their relationship to the neonatal birth weight[J]. Nutr J, 2020, 19(1): 89. DOI: 10.1186/s12937-020-00637-6.
[17]	OOSTINGH E C, DE VOS I, HAM A C, et al. No independent associations between preconception paternal dietary patterns and embryonic growth: the predict study[J]. Clin Nutr, 2019, 38(5): 2333-2341. doi: 10.1016/j.clnu.2018.10.011
[18]	ZHANG X, WU S, QI X, et al. Effect of paternal body mass index on In Vitro fertilization and neonatal outcomes among oligozoospermia and asthenospermia patients[J]. World J Mens Health, 2024, 42(1): 216. DOI: 10.5534/wjmh.220286.
[19]	GUO T, YANG Y, JIA J, et al. Preconception paternal/maternal BMI and risk of small/large for gestational age infant in over 4.7 million Chinese women aged 20-49 years: a population-based cohort study in China[J]. Br J Nutr, 2023, 129(2): 365-366. doi: 10.1017/S0007114522001179
[20]	CLAYCOMBE L K G, BUNDY A N, ROEMMICH J N. Paternal high-fat diet and exercise regulate sperm miRNA and histone methylation to modify placental inflammation, nutrient transporter mRNA expression and fetal weight in a sex-dependent manner[J]. J Nutr Biochem, 2020, 81: 108373. DOI: 10.1016/j.jnutbio.2020.108373.