Establishing twin birth weight percentile values with gestational age of 22 to 42 weeks in China: a synthesis study of curve modelling

Abstract

A twin-based chart of birth weight was useful to avoid overdiagnosis of small for gestational age (SGA) or large for gestational age (LGA) in twins comparing to the singleton-based chart. Several twin-based charts of birth weight were reported based on large data from nationwide, multiple cities or local regions in China. However, these twin-based charts showed inconsistent ranges across gestational age (GA) and not well-consistent growth trajectories, and may require further integration. We aimed to develop a set of smoothed percentile growth curves of twin birth weight in Chinese neonates that allows for continuous use from extremely preterm to full-term. We collected twin birth weight data through a procedure of systematic review by searching PubMed, Scopus, Web of science, Chinese national knowledge infrastructure (CNKI), and Wanfang from their inception to April 30, 2025. Finally, five studies that met the inclusion criteria were included in this present study. We used a two-stage proportionally weighted approach to generate initial integrated percentile data of twin birth weight, and then employed polynomial regression equation and the LMS method to establish the P₃, P₁₀, P₂₅, P₅₀, P₇₅, P₉₀, and P₉₇ reference values of twin birth weight that allowed for continuous use from GA of 22 to 42 weeks in Chinese male and female twins. Our established twin-based growth curves illustrated a distinct pattern comparing to the singleton-based growth curves in China. In conclusion, our established twin-based birth weight percentile references could be preferred over the use of singleton references when diagnosing SGA or LGA in twin newborns or monitoring the growth of twin newborns in China.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-28289-8.

Introduction

Birth weight is the most commonly used proxy of newborn growth and development. Newborn babies are routinely classified as small for gestational age (SGA) or large for gestational age (LGA) if their birth weight percentile falls below the 10th percentile or above the 90th percentile of the growth references. SGA or LGA are often at high risk for perinatal mortality and morbidity as well as long-term health problems. One of the hallmarks of twin pregnancies is the slower rate of fetal growth and development when compared to singleton pregnancies. As a consequence, birth weight of twins is significantly lower than singletons beginning around 30 to 32 weeks of gestational age (GA)¹. Therefore, an unbiased classification for twin newborns is of great significance in neonate clinical practice.

Based on existing evidence, the relative smallness of twins is probably the result of benign adaptive mechanisms and the normal deceleration of growth in twins cannot be attributed to a pathological slowing, it is likely preferable to use a twin-based chart of birth weight to avoid overdiagnosis of intrauterine growth restriction in twin pregnancies². In pursuit of this goal, several countries have constructed their twin-based chart of birth weight by sex and GA^3–6. In recent years, the singleton-based chart of birth weight has been established and recommended for use in China^7,8. A retrospective cohort study in China suggested that the use of twin-based chart of birth weight seemed more reasonable and may be preferred over the use of singleton chart when diagnosing SGA in twin fetuses⁹. Several twin-based charts of birth weight were reported based on large data from nationwide, multiple cities or local regions in China from 2006 to 2015 to 2010-2021¹⁰⁻¹⁴. However, these twin-based charts showed inconsistent ranges across GA and their corresponding growth trajectories did not appear to be entirely robust, and thus further integration may be necessary and realistic in practical application.

We aimed to establish the percentile values of twin birth weight by sex and GA based on recently published high-quality data on Chinese twin newborns in order to produce more robust and reliable reference values for identifying intrauterine growth restriction or postnatal growth deviation from normal trajectory in Chinese twin neonates.

Methods

Data source and search strategy

We conducted the literature search on PubMed, Scopus, Web of science, Chinese National Knowledge Infrastructure (CNKI), and Wanfang from their inception to April 30, 2025, with no language restrictions. The following search terms expressed in the Boolean form were used: [(twin*) AND ((birth weight) OR (birthweight)) AND ((percentile*) OR (curve*) OR (reference*) OR (standard*)) AND ((China) OR (Chinese))]. The detailed search strategy was described in Table S1. We have applied a registration in the International Prospective Register of Systematic Reviews (PROSPERO) database (No. 477530).

Inclusion and exclusion criteria

Studies that met the following criteria simultaneously were included in this present study: (1) GA was defined as last-menstrual-period and/or ultrasound examination; (2) the reference values of twin birth weight included the frequently-used percentile values by sex and GA; (3) Both male and female sample sizes were greater than 1000; (4) the study subjects were Chinese neonates. Studies were excluded if any one of the following criteria was met: (1) repeated reports or identical data in different reports; (2) ultrasound-based fetal weight; (3) only monochorionic and/or dichorionic twin birth weight; (4) only primipara and/or multipara twin birth weight.

Data extraction and quality assessment

Two investigators (XNZ and CDY) extracted data independently. Titles and abstracts of identified articles were screened and irrelevant articles were removed. If necessary, full texts of articles were examined thoroughly for their eligibility. A third investigator (WS) was consulted in case of disagreement, and a 100% consensus was reached. Data were entered into the Microsoft Excel 365, including the first author, published year, surveyed year, surveyed location, sample size, GA range, method for assessing GA, method for establishing percentile curves, and the percentile values of twin birth weight by sex and GA.

Potential eligible articles were assessed by the QualSyst quality assessment tool, and each study was assessed for risk of bias by 14 criteria¹⁵. Each item was scored, depending on the degree to which specific criteria were met or reported (“yes” = 2; “partial” = 1; “no” = 0). The items not applicable to a particular study were marked “N/A” and excluded from the calculation of the summary score. The final score was calculated by summing up the total score across the relevant items, expressed as a percentage of the available theoretical maximum. The quality of the articles was categorized as strong (> 75%), moderate (55–75%), and weak (< 55%).

Data synthesis and curve modeling

Figure 1 presented the flowchart of inclusion/exclusion of the search records. Finally, five eligible studies were included in this present study, one was the Nationwide Birth Defects Surveillance System data (GA of 28–42 weeks)¹⁰, one was a multicenter study (GA of 25–40 weeks)¹¹, two were single region studies (GA of 25–42 weeks vs. 26–42 weeks)^12,13, and one was the National Vital Statistics System data (GA of 22–27 weeks)¹⁴.

Fig. 1.

Flowchart of inclusion/exclusion of the search records.

Figure 2 showed brief procedure of data synthesis and curve modeling, consistent with the procedure and steps of previous studies^16,17. First, we computed the synthetized values for each of the P₃, P₁₀, P₂₅, P₅₀, P₇₅, P₉₀, P₉₇ of twin birth weight by sex and GA according to the two-stage proportionally weighted approach. In the first stage, two studies on single region were combined to the multicenter study according to equal weight by center/region. In the second stage, those above synthesized data in the first stage were further combined to those two national studies by a proportional weight of sample sizes by sex. A sensitivity analysis of data synthesis was also conducted as a direct equal weight of each of the included five studies irrespective of study location and sample sizes. Second, in the procedure of curve modeling, the initial smoothed curves were produced by a manual adjustment for the initial integrated data from two-stage proportionally weighted approach. These initial smoothed curves were further fitted by quartic polynomial regression equation to generate the intermediate smoothed curves. After that, the LMS parameters were produced based on these intermediate smoothed curves by the nonlinear equation from the LMS method¹⁸:

Fig. 2.

Procedure of data synthesis and curve modeling.

where C_100α(t) is the centile curve plotted against age t, z_α is the normal equivalent deviate for the centile (for example when α = 0.97 corresponding to P₉₇, z_α = 1.88), and L(t), M(t) and S(t) are the fitted smoothed curves plotted against age. Third, the final smoothed curves and any percentile values by sex and GA were established by the LMS parameters according to the equation of the LMS method.

Statistical analysis

Two-stage proportionally weighted percentile values of twin birth weight were calculated as the initial integrated data that were then manually adjusted to obtain the initial smoothed curves. A polynomial regression equation was employed to fit the initial smoothed curves and then generate the intermediate smoothed curves. After that, the LMS method was used to fit the intermediate smoothed curves and then produce the LMS parameters that were used to calculate any percentile values by sex and GA as the final smoothed curves. R-square and adjusted R-square as well as Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess goodness of fit of the growth curves by means of the PROC GLMSELECT procedure. The P₃, P₁₀, P₅₀, P₉₀, P₉₇ values of our finally established smoothed curves of twin birth weight were compared with the corresponding percentiles of twin birth weight from the included five studies^10–14 and from other countries (USA (1980–1990 s), Germany (2007–2011), Australia (2001–2010), and Japan (1968–1990))^3-6, as well as the corresponding percentiles of singleton birth weight from a representative sample of China^7,8. Data procedure and statistical analysis were performed by SAS 9.4 (SAS Institute Inc., Cary, North Carolina).

Results

The literature search identified 236 papers, of which 229 were excluded as not relevant to this study or no reference values or intrauterine fetal data or duplicate records (Fig. 1). The remaining seven studies were examined in details, however of which two were further discarded as incomplete or identical data. Finally, five studies that met the inclusion criteria were included in this present study (Table 1), with strong quality according to the QualSyst quality assessment tool (Table S2). The original P₃, P₁₀, P₂₅, P₅₀, P₇₅, P₉₀, P₉₇ percentile values of twin birth weight by sex and GA for these included five studies were extracted and documented in Tables S3 to S7.

Table 1.

Details of the included five studies.

	Dai, et al.10	Huang, et al.11	Miao, et al.12	Zhang, et al.13	Song, et al.14
Published year	2017	2022	2019	2016	2024
Surveyed year	2006–2015	2017–2020	2014–2017	2006–2015	2010–2021
Surveyed location	30 provinces or municipalities in China	11 cities from 8 provinces of China	Guangdong province in southern China	Wuhan city (capital of Hubei province) in central China	68 tertiary NICUs from 31 provinces in China
Data source	Nationwide Birth Defects Surveillance System data	Maternal and child health care hospitals in 11 cities of China	Guangdong Provincial Birth Certificate System	Wuhan Birth Registry Information System	National Vital Statistics System data
Method for assessing gestational age	Last-menstrual-period	Last-menstrual-period and ultrasound examination	Last-menstrual-period and ultrasound examination	Last-menstrual-period and ultrasound examination	Last-menstrual-period and ultrasound examination
Gestational age range (weeks)	28–42	25–40	25–42	26–42	22–27
Total sample size	54,786	17,256	161,076	22,507	3232
No. of boys	28,202	8940	84,208	11,861	1919
No. of girls	26,584	8316	76,868	10,646	1313
Smoothing method	LMS method	LMS method	LMS method	LMS method	LMS method

Based on the included five studies, the two-stage proportionally weighted percentile values of twin birth weight were calculated and then used to produce the initial integrated data for further curve modelling (Fig. 3). A sensitivity analysis illustrated that the curve shape from the two-stage proportionally weighted approach was similar to the curve shape from the equally weighted approach, however, the curves from the two-stage proportionally weighted approach seemed more conservative and inclusive, especially at the younger and older GA (Fig. S1).

Fig. 3.

Comparisons of the 3rd, 10th, 50th, 90th, 97th percentile curves of twin birth weight among final smoothed curves, manual smoothed curves, and original weighted curves.

A polynomial regression equation was employed to fit the initial manual smoothed percentile values that was an optimization to the two-stage proportionally weighted curves, showing good fit and tolerable difference for the polynomial fitted smoothed curves (Fig. S2). According to the LMS method, the final smoothed percentile curves were produced from the fitted LMS values that were generated from the intermediate polynomial fitted smoothed percentile values. The 3rd, 10th, 50th, 90th, 97th percentile curves exhibited robust fit and good consistency among final smoothed curves, manual smoothed curves, and original weighted curves (Fig. 3). Table 2 presented the LMS parameters and frequently-used percentile values of birth weight by sex and GA of 22 to 42 weeks in Chinese twins, and other percentile values can be calculated by the LMS parameters according to the equation of the LMS method. Tables S8 to S9 further presented the LMS parameters and percentile values of birth weight by sex and GA in exact weeks and days for more exact growth evaluation and exact calculation of z-scores for individual values. In order to facilitate actual application, we drew a set of user-friendly growth charts for male and female twin newborns which were composed of the frequently-used seven main percentile curves of twin birth weight in the charts (Figs. 4 and 5).

Table 2.

The L, M and S parameters and percentile values of birth weight by sex and gestational age in Chinese twins.

Gestational age (weeks)	Boys
	L	M	S	Percentiles (birth weight in g)
				P3	P10	P25	P50	P75	P90	P97
22	0.0903	491.6782	0.1137	396	425	455	492	531	568	608
23	0.2049	600.0984	0.1260	471	509	551	600	653	703	756
24	0.3467	699.5653	0.1396	531	582	636	700	767	832	899
25	0.5702	799.5428	0.1542	582	648	718	800	885	964	1046
26	0.7918	906.8652	0.1666	633	718	806	907	1010	1105	1200
27	0.9674	1025.9837	0.1751	690	797	905	1026	1147	1257	1366
28	1.0714	1159.2126	0.1787	764	891	1019	1159	1298	1423	1545
29	1.1305	1306.9751	0.1781	858	1004	1149	1307	1463	1601	1736
30	1.1604	1468.0498	0.1746	971	1133	1293	1468	1639	1791	1939
31	1.1703	1639.8166	0.1692	1101	1277	1451	1640	1825	1989	2149
32	1.1665	1818.5032	0.1630	1244	1431	1617	1819	2017	2192	2363
33	1.1548	1999.4307	0.1565	1395	1592	1787	1999	2209	2395	2576
34	1.1408	2177.2603	0.1504	1547	1751	1955	2177	2397	2592	2782
35	1.1304	2346.2389	0.1451	1693	1904	2115	2346	2574	2778	2976
36	1.1286	2500.4460	0.1409	1825	2043	2261	2500	2737	2947	3153
37	1.1381	2634.0389	0.1379	1937	2162	2387	2634	2878	3094	3306
38	1.1580	2741.4998	0.1364	2022	2255	2487	2741	2992	3215	3432
39	1.1830	2817.8812	0.1364	2076	2317	2556	2818	3075	3303	3526
40	1.2027	2859.0523	0.1382	2093	2343	2590	2859	3123	3357	3585
41	1.2033	2861.9455	0.1420	2074	2331	2585	2862	3133	3374	3608
42	1.1690	2824.8019	0.1479	2018	2280	2541	2825	3104	3352	3594

Gestational age (weeks)	Girls
	L	M	S	Percentiles (birth weight in g)
				P3	P10	P25	P50	P75	P90	P97
22	2.1303	478.3299	0.1032	372	410	444	478	510	537	563
23	1.7476	580.6918	0.1218	433	484	531	581	627	667	704
24	1.2770	671.1285	0.1398	487	548	607	671	734	789	842
25	1.1648	760.6750	0.1586	527	603	679	761	841	913	983
26	1.1260	857.3161	0.1730	572	664	757	857	957	1045	1131
27	1.1203	966.2889	0.1817	628	738	847	966	1084	1188	1291
28	1.1304	1090.3864	0.1848	701	828	953	1090	1225	1345	1462
29	1.1482	1230.2597	0.1834	793	935	1077	1230	1381	1515	1645
30	1.1685	1384.7212	0.1788	903	1061	1216	1385	1550	1696	1839
31	1.1873	1551.0473	0.1724	1030	1200	1369	1551	1730	1887	2041
32	1.2006	1725.2812	0.1652	1169	1351	1531	1725	1915	2083	2247
33	1.2047	1902.5361	0.1581	1317	1508	1697	1903	2103	2281	2453
34	1.1967	2077.2977	0.1517	1465	1665	1863	2077	2288	2474	2655
35	1.1756	2243.7271	0.1463	1609	1815	2020	2244	2463	2658	2848
36	1.1434	2395.9638	0.1421	1742	1953	2165	2396	2624	2827	3025
37	1.1059	2528.4285	0.1394	1855	2072	2289	2528	2765	2976	3183
38	1.0730	2636.1259	0.1381	1944	2166	2390	2636	2881	3100	3315
39	1.0578	2714.9477	0.1383	2003	2231	2461	2715	2968	3194	3416
40	1.0751	2761.9751	0.1402	2026	2262	2500	2762	3022	3255	3484
41	1.1398	2775.7821	0.1435	2011	2258	2505	2776	3043	3280	3512
42	1.2649	2756.7381	0.1483	1954	2218	2477	2757	3029	3269	3501

Gestational age represents the age group in weeks (e.g., 24 weeks represent 24 weeks + 0 day to + 6 days).

Fig. 4.

Twin birth weight growth charts for male newborns in China.

Fig. 5.

Twin birth weight growth charts for female newborns in China.

Our finally established smoothed twin-based curves seemed a reliable projection of the cluster of the original curves from the included five studies (Fig. 6). The growth trajectory of our established twin-based curves of birth weight was generally consistent with the growth trajectories of twin birth weight from USA, Germany, Australia, and Japan, but was lower than those from Germany and Australia at > 36 weeks of GA and higher than those from USA and Japan at > 32 weeks (Fig. 7). In addition, our established twin-based curves of birth weight were obviously lower than the singleton-based curves from a representative sample of China, with a more pronounced trend after 32 weeks of GA (Fig. 8).

Fig. 6.

Comparisons of the 3rd, 50th, 97th percentile curves of twin birth weight among final smoothed curves, and original smoothed curves from the included five studies.

Fig. 7.

Comparisons of the 10th, 50th, 90th percentile curves of twin birth weight among China, USA, Germany, Australia and Japan.

Fig. 8.

Comparisons of the 3rd, 10th, 50th, 90th, 97th percentile curves of birth weight among twin-based curves (this study) and singleton-based curves in China.

Discussion

Our established twin-based percentile values of birth weight may be useful to identify those twin newborns at risk of adverse outcomes and may be preferred over the use of singleton growth charts when monitoring the growth trajectories of twin newborns. Compared to the use of singleton-based charts for twin neonates, using twin-based charts for twin neonates has the potential to avoid overdiagnoses of intrauterine growth restriction and the consequences of this misdiagnosis.

The mechanisms of fetal growth deceleration in twins and whether this phenomenon represents pathology or physiological adaptation has been the subject of debate¹⁹. Some organizations recommended the use of twin-based charts²⁰, whereas others recommended singleton charts²¹. Our comparisons demonstrated a distinct variation between twin-based and singleton-based charts in Chinese populations (Fig. 8), suggesting that the use of singleton-based charts may lead to a high estimation of suspected growth restricted twin neonates because of what seems to fall off the singleton-based charts. Considering that the use of twin-based charts can lead to a considerable reduction in the proportion of twin neonates identified as SGA and such charts could safely reduce the burden of unnecessary medical interventions in twin pregnancies², we thus tend to propose the use of twin-based charts for the diagnosis of SGA or LGA of twin neonates in clinical practice.

We selected eligible high-quality studies including percentile values of twin birth weight to contribute this present study according to the procedure of systematic review. We synthesized the percentile values of twin birth weight by a two-stage proportionally weighted approach and finally produced smoothed percentile curves by a series of curve modeling procedure including manually smoothing the weighted data, using polynomial equation for fitting manual smoothed curves, using the LMS method for fitting polynomial smoothed curves, and ultimately establishing a set of standardized growth curves of twin birth weight. This curve modeling procedure we used was consistent with the procedure used to establish international and China postnatal growth monitoring curves for preterm infants^16,22.

Compared to the percentile curves of singleton-based birth weight in Chinese newborns^7,8, our established twin-based curves were obviously lower, especially for newborns at GA of ≥ 32 weeks, supporting that the twin-based charts may be more appropriate and could be preferred for use in assessing and monitoring the growth of twin newborns. A model-fitting analysis showed twin birth weight varies across racial groups²³. A reference from a country may not be optimal fits to other countries. Our present study has once again confirmed this variation of growth in twin birth weight by comparing to other countries, supporting the necessity of establishing the percentile values of twin birth weight in China. We argued that a flat or even reversed trajectory of twin birth weight in late full-term (e.g., 40–42 weeks) may be due to the fact that a proportion of indeed smaller newborns can continue to this final stage. However, further in-depth studies are still needed. We also noticed that a sharp increasing trend at term 40 weeks in Huang’s study that seemed different from other studies, may partially attribute to the right-edge effect in curve fitting^24,25.

Our study has several strengths. First, we constructed robust and reliable percentile values based on all available high-quality data in Chinese twin newborns searched according to the procedure of systematic review. Second, we synthesized the data by two-stage proportionally weighted approach and additionally conducted a sensitivity analysis by equally weighted approach. However, our study has several limitations. First, although we used twin-based references without disaggregation of monochorionic/dichorionic twins for more common use in practice, the subtle difference may exist between monochorionic and dichorionic twin birth^26,27. However, a recent cohort study including 398 twin pregnancies indicated that the differences of fetal weight between monochorionic and dichorionic twins were clinically insignificant²⁸ Second, the unavailable data at extreme GA (e.g., Dai’s study) may affect the new combined reference values at extreme GA (e.g., 26 weeks of GA), so we considered using a two-stage proportionally weighted approach and further using a sensitivity analysis for examining robustness. Third, it is still essential to acknowledge that ultrasound-derived growth trajectories hold greater clinical relevance than birth-weight-based references. This is particularly true for preterm deliveries where fetuses fail to achieve their full growth potential. Fourth, since our present study did not exclude pregnancies complicated by hypertensive disorders, fetal anomalies, or other confounders, the resulting curves should be framed as population-based reference rather than optimal growth standard trajectories. Fifth, indeed, the reference values at term 40, 41 and 42 weeks remained uncertain due to selection biases and/or model fitting, the actual use should be caution beyond term 40 weeks.

Conclusions

Our established twin-based percentile reference values of birth weight could be preferred over the use of singleton references when diagnosing SGA or LGA in twin newborns or monitoring the growth of twin newborns in China. Further studies are needed to test the performance and benefit of our established twin-based charts in Chinese twin newborns and provide a better understanding of the mechanisms responsible for the slower growth in twins.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

XNZ conceptualized and designed the study, supervised data collection and data analyses and drafted the initial manuscript; CDY participated in study design, data collection and data analyses and reviewed the manuscript; WS participated in study design, coordinated data collection and reviewed the manuscript; HHW participated in study design and reviewed the manuscript; YQZ participated in study design and reviewed the manuscript. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Data availability

All data generated or analyzed during this study are included in this present article.

Declarations

Competing interests

The authors declare no competing interests.