Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China

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Original research

Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China Jinnan Liu a,1 , Shuting Wang b,1 , Junhong Leng b , Jing Li a , Xiaoxu Huo a , Liang Han a , Jin Liu b , Cuiping Zhang b , Juliana C.N. Chan c , Zhijie Yu d , Gang Hu e , Xilin Yang a,f,g,∗ a

Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China Project Office, Tianjin Women and Children’s Health Center, Tianjin, China c Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity and The Chinese University of Hong Kong-Prince of Wales Hospital-International Diabetes Federation Centre of Education, The Chinese University of Hong Kong, Hong Kong SAR, China d Population Cancer Research Program and Department of Pediatrics, Dalhousie University, Halifax, Canada e Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA f Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China g Tianjin Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China b

a r t i c l e

i n f o

Article history: Received 5 September 2019 Received in revised form 15 December 2019 Accepted 17 December 2019 Available online xxx Keywords: Gestational diabetes mellitus Quality of life Maternal age Chinese pregnant women

a b s t r a c t Aims: This study aimed to examine impacts of gestational diabetes mellitus (GDM) on quality of life (QoL) domains in Chinese pregnant women. Methods: We recruited 13,358 pregnant women in Tianjin, China. GDM was diagnosed using the criteria of International Association of Diabetes and Pregnancy Study Group. QoL was measured using the 36-Item Short-Form Health Survey. General linear model was used to obtain ␤-coefficient and 95% confidence intervals (CI) of GDM for QoL domain and summary scores. Results: 7.25% of the pregnant women developed GDM. Among the QoL domain and summary scores, only general health (GH) score was lower in the GDM group than in the non-GDM group. GDM and advanced maternal age (i.e., ≥ versus <30 years) were negatively associated with GH in multivariable analyses (␤coefficient: −1.17, 95%CI: −2.17 to −0.17 & −0.79, −1.40 to −0.18, respectively). In subgroup analyses, the ␤-coefficient of GDM for GH among women with maternal age ≥30 years was enhanced to −2.17 (−3.94 to −0.40) in multivariable analysis while the ␤-coefficient of GDM for GH among women aged <30 years was attenuated to non-significance. Conclusions: GDM and advanced maternal age were associated with reducing GH, and presence of advanced maternal age markedly increased the effect of GDM on GH. © 2019 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

1. Introduction In the recent three decades, the prevalence of gestational diabetes mellitus (GDM) has experienced a rapid increase in the world, in particular in Asian countries, due to rapid urbanization and increasing prevalence of sedentary lifestyles [1,2]. The increased prevalence of GDM and its associated long-term health outcomes in the mother and offspring has become a high global medical burden [3]. Women with GDM are at higher risk of pregnancy-induced hypertension and preeclampsia during the index pregnancy and

∗ Corresponding author at: Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin 300070, China. E-mail addresses: [email protected], [email protected] (X. Yang). 1 Equal contribution to the manuscript.

adverse pregnancy outcomes [4,5], and at markedly increased risk of diabetes [6] and cardiovascular diseases [7] later in life. Offspring born to mothers with GDM during the index pregnancy are at higher risk of macrosomia and morbidities in the short run [8], and childhood obesity [9] and diabetes [10] in the long run. Quality of life (QoL), as defined by the World Health Organization, is an individual’s perception of their position in life in the culture, value system and measured wellbeing related to various domains such as physical, mental and social functioning [11]. Indeed, the QoL measure has been used widely in outcomes research. Many chronic diseases such as diabetes [12], stroke [13], cardiovascular diseases [14] and cancer [15] were reported to substantially reduce QoL. On the other hand, a few studies have evaluated associations of GDM with QoL and its domains during pregnancy, with inconclusive findings. For example, a small study of Italian pregnant women who attended diabetes clinics (n of GDM

https://doi.org/10.1016/j.pcd.2019.12.004 1751-9918/© 2019 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

Please cite this article in press as: J. Liu, et al., Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.12.004

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versus normal control: 176 vs. 39) showed that GDM women in the third trimester of pregnancy had a poor general health score but a better physical component score than their controls [16]. Another small study from Australia (n of GDM versus normal control: 62 vs. 124) reported that GDM women had poor scores in vitality and general health perception after the screening for GDM and the former persisted into late pregnancy [17]. On the contrary, a small study from the US reported that women with GDM had similar QoL and its domains although women with pregnancy induced hypertension had significant decline in physical function and vitality from prepregnancy to postpartum [18]. Indeed, whether GDM has negative impacts on QoL during pregnancy remains inconclusive. From December 1998 to December 1999, our group launched the Tianjin Study of Diabetes in Pregnancy, introducing a universal GDM screening and management system in urban Tianjin, China [19]. Based on this system, we further set up a cohort of pregnant women to screen and manage GDM through a 3-tier prenatal care system in urban Tianjin from October 2010 to August 2012 [2]. Using this population-based prospective cohort, we aimed to explore the association between GDM and QoL in Chinese pregnant women in urban Tianjin, China. 2. Methods 2.1. Study participants and setting Tianjin, as one of the four municipalities directly under administration of the central government of China, has 12 million residents and about 5 million residents live in the six central urban districts of Tianjin. The details of the study participants and methods had been described previously [2]. In brief, all pregnant women were first registered at primary care hospitals which were close to their residence from October 2010 to August 2012. At 24th to 28th gestational weeks, these women were offered a non-fasting 50-g 1-h glucose challenge test (GCT) at a primary care hospital and a standard fasting 75-g 2-h oral glucose tolerance test (OGTT) at Tianjin Women and Children’s Health Center (TWCHC) if their GCT was ≥7.8 mmol/L. During this period, 22,302 pregnant women were registered at their first antenatal care visit. After excluding 7,635 women who did not fill in the questionnaire on QoL, 482 women with missing more than half of the items on QoL, 12 women who had diabetes before pregnancy, 268 women who did not have GCT and 547 women who had a positive GCT but did not undergo an OGTT, the remaining 13,358 women were included in the current analyses. This research protocol was approved by Ethics Committee for Clinical Research of TWCHC and written informed consent was obtained from all pregnant women before data collection. 2.2. Identification of GDM A two-step procedure was used to identify GDM among pregnant women at 24th to 28th weeks of pregnancy. All women were offered a 50-g 1-h GCT in non-fasting status. Women who had plasma glucose (PG) in GCT ≥7.8 mmol/L were referred to the GDM clinic at TWCHC where they underwent a standard 75-g 2-h OGTT in the morning. After overnight fasting of at least 8 h, blood samples were taken at fasting, 1-h and 2-h after ingestion of the glucose load. GDM was defined using the International Association of Diabetes and Pregnancy Study Group criteria, i.e., GDM is diagnosed if any of the following criteria having been met: fasting PG ≥5.1 mmol/L or 1-h PG ≥10.0 mmol/L or 2-h PG ≥8.5 mmol/L [20]. 2.3. Assessment of QoL domains The 36-Item Short-Form Health Survey (SF-36) is a validated tool to detect QoL in general as well as pregnant women popu-

lations. For example, SF-36 was used to assess QoL in Italian and Australian pregnant women [16,17]. In this regard, SF-36 was also validated for use in Chinese general and pregnant women populations [21,22]. SF-36 has 36 items and covers eight separate domains of QoL: physical functioning (PF), role physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Each domain score ranges from 0 (the worst possible health state) to 100 (the best possible health state) [23]. Two summary scores including physical component summary (PCS) and mental component summary (MCS) are calculated with eight domain scores based on the Chinese scoring algorithm [21]. SF-36 would be regarded as invalid if more than half of the items had missing values. If less than half of the items in SF-36 had missing values, the missing score can be imputed as the mean score of each item. In this study, QoL of pregnant women was measured at the GCT time using SF-36 which was administered at primary care hospital by self-completion. 2.4. Assessment of covariates As in previous studies [2,24], data were retrieved from the main database of the Maternal and Child Health Information System or collected using a series of questionnaires from the first antenatal care visit and GCT time to postpartum. The measured parameters included maternal age, height, weight, systolic/diastolic blood pressure (SBP/DBP), parity, educational attainment, family income per month, weight gain to GCT, current smoker before/during pregnancy, alcohol drinker before/during pregnancy at the first antenatal care visit and at the GCT time. Pre-pregnancy body mass index (BMI) was calculated as body weight in kilogram at the first antenatal care visit divided by the squared height in meter and categorized as underweight (<18.5 kg/m2 ), normal weight (≥18.5 to <24 kg/m2 ), overweight (≥24 to <28 kg/m2 ) and obesity (≥28 kg/m2 ) using the criteria of Working Group on Obesity in China [25]. Per capita family income was calculated as total family income per month divided by number of household members and categorized as low income (≤¥1500 per month), low-middle income (>¥1500 to ≤2000 per month), middle-high income (>¥2000 to ≤4000 per month) and high income (>¥4000 per month) [26]. Weight gain to GCT was calculated as the difference of body weight divided by the difference of gestational weeks between the first registration and GCT time. 2.5. Statistical analyses Statistical Analysis System (release 9.4, SAS Institute, Cary, NC), unless specified, was used to perform analyses and a two-tailed P value <0.05 was considered to be statistically significant. Q–Q plots were used to test the normal distribution of continuous variables. Variables in normal distribution were presented as means ± standard deviation (SD). Differences between the GDM group and the non-GDM group were compared using unpaired Student t-test if the normality was not rejected. On the other hand, variables whose normality was rejected were presented as median (25th–75th percentile) and their differences between groups were compared using Two Sample Wilcoxon rank-sum test. Categorical variables were presented as number (percentages). Chi-square test was used to compare their differences between groups. General linear model was used to obtain ␤-coefficient and 95% confidence intervals (CI) of GDM for QoL domain and summary scores. A structured adjustment scheme was used to show robustness of the results. First, we obtained ␤-coefficient and 95%CI of GDM for QoL domain and summary scores in the univariable analyses. Second, we adjusted for traditional or potential factors associated with QoL to control for possible confounding effects. These potential confounders included maternal age, pre-pregnancy BMI, SBP, parity, per capital fam-

Please cite this article in press as: J. Liu, et al., Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.12.004

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Table 1 Clinical and biochemical characteristics of participants according to occurrence of gestational diabetes mellitus. Non-GDM (n = 12,389) Variables at registration Age, year Age categories, year <30 ≥30 BMI, kg/m2 BMI categories, kg/m2 <18.5 ≥18.5 to <24 ≥24 to <28 ≥28 Gestational age at first antenatal care visit, week Systolic blood pressure, mmHg Diastolic blood pressure, mmHg Education attainment ≥12 year Parity ≥1 Per capita family income Low Low-middle Middle-high High Current smoker before pregnancy Alcohol drinker before pregnancy Variables at GCT Gestational age at GCT, week Weight gain to GCT, kg/week Current smoker during pregnancy Alcohol drinker during pregnancy Domain and summary scores Physical functioning Role physical Bodily pain General health Vitality Social functioning Role emotional Mental health Physical component summary Mental component summary

GDM (n = 969)

P value

28.4±2.9

29.5±3.2

<0.0001* <0.0001**

9,412(75.97) 2,977(24.03) 22.2±3.3

637(65.74) 332(34.26) 24.2±4.0

1,264(10.20) 8,051(64.99) 2,326(18.77) 748(6.04) 10.4±2.3 105.0±10.7 68.2±7.7 6,742(54.42) 453(3.66)

38(3.92) 477(49.23) 310(31.99) 144(14.86) 10.3±2.2 108.3±11.3 70.5±8.1 529(54.59) 46(4.75)

3,292(26.57) 1,756(14.17) 5,581(45.05) 1,760(14.21) 384(3.10) 3,837(30.97)

253(26.11) 155(16.00) 428(44.17) 133(13.73) 51(5.26) 335(34.57)

0.0003** 0.0198**

24.7±2.7 0.5±0.3 106(0.86) 126(1.02)

24.9±1.9 0.5±0.3 13(1.34) 12(1.24)

0.0313* 0.8410* 0.1210** 0.5116**

75(60–85) 50(25–100) 84(74–100) 87(72–97) 80(70–85) 89(78–100) 100(67–100) 80(72–88) 45(40–51) 57(50–62)

75(60–85) 50(25–100) 84(74–100) 85(72–95) 80(70–85) 89(78–100) 100(50–100) 80(68–88) 45(39–51) 56(50–62)

0.5697*** 0.1778*** 0.5504*** 0.0276*** 0.9728*** 0.2839*** 0.1123*** 0.7975*** 0.3426*** 0.4586***

<0.0001* <0.0001**

0.1246* <0.0001* <0.0001* 0.9170** 0.0847** 0.4828**

Abbreviations: GDM, gestational diabetes mellitus; BMI, body mass index; GCT, glucose challenge test. Data are reported in mean ± SD or frequency (%) or median (25th–75th percentile). * Derived from unpaired Student’s t-test. ** Derived from Chi-square test. *** Derived from Wilcoxon rank sum test.

ily income, educational attainment, current smoker before/during pregnancy, alcohol drinker before/during pregnancy, weight gain to GCT and GDM. In addition, we performed subgroup analysis to examine subgroup effects of GDM on QoL domain and summary scores by maternal age, i.e., <30 years and ≥30 years. 3. Results 3.1. Characteristics of the study participants Among 13,358 pregnant women at registration, mean maternal age was 28.5 (SD: 2.9) years, mean height was 163.2 (SD: 4.7) cm, mean pre-pregnancy BMI was 22.3 (SD: 3.4) kg/m2 , mean SBP/DBP was 105.3/68.3 (SD: 10.8/7.8) mmHg, mean gestational age at first registration was 10.4 (SD: 2.3) weeks, 3.3% (n = 435) smoked before pregnancy, 31.2% (n = 4,172) drank before pregnancy, and 7.3% (n = 969) developed GDM. In addition, the median PF score was 75 (25th–75th percentile: 60–85), RP score was 50 (25–100), BP score was 84 (74–100), GH score was 87 (72–97), VT score was 80 (70–85), SF score was 89 (78–100), RE score was 100 (67–100), MH score was 80 (68–88), PCS score was 45 (39–51) and MCS score was 57 (50–62). GH score was lower in the GDM group than in the nonGDM group while other domain and summary scores were similar in the two groups (Table 1).

3.2. Risk associations of GDM and maternal age with QoL domain and summary scores GDM was negatively associated with GH in univariable and multivariable analyses (adjusted ␤-coefficient: −1.17, 95%CI: −2.17 to −0.17). On the other hand, GDM was not associated with other QoL domain and summary scores in univariable and multivariable analyses (Table 2). Advanced maternal age (i.e., ≥ versus <30 years of age) was negatively associated with PF, RP, GH, SF, MH and PCS in univariable and multivariable analyses (adjusted ␤-coefficient of PF: −1.38, 95%CI: −2.04 to −0.72; RP: −4.23, −5.77 to −2.69; GH: −0.79, −1.40 to −0.18; SF: −1.15, −1.84 to −0.45; MH: −0.60, −1.18 to −0.01 and PCS: −0.92, −1.24 to −0.59) (Table 3). 3.3. Subgroup analyses of GDM for QoL In subgroup analyses, the ␤-coefficient of GDM for GH among women with maternal age ≥30 years was enhanced to −2.17 (−3.94 to −0.40) in multivariable analysis while the ␤-coefficient of GDM for GH among women aged <30 years was attenuated to nonsignificance (adjusted ␤-coefficient: −0.69, 95%CI: −1.91 to 0.52) (Table 4). Among women with maternal age <30 years, however, GDM was negatively associated with RE in multivariable analysis (␤-coefficient: −2.97, 95%CI: −5.72 to −0.21).

Please cite this article in press as: J. Liu, et al., Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.12.004

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Table 2 The association of gestational diabetes with domain and summary scores of quality of life. Dependent variable Physical functioning Role physical Bodily pain General health Vitality Social functioning Role emotional Mental health Physical component summary Mental component summary

Model 1 ␤ (95%CI) −0.32(−1.36 to 0.72) −1.65(−4.08 to 0.78) 0.29(−0.55 to 1.14) −1.04(−2.00 to −0.08) −0.15(−1.07 to 0.77) −0.59(−1.68 to 0.51) −1.65(−3.84 to 0.53) −0.01(−0.93 to 0.92) −0.27(−0.78 to 0.24) −0.27(−0.85 to 0.32)

Model 2 ␤ (95%CI) −0.32(−1.40 to 0.76) −1.59(−4.11 to 0.93) 0.28(−0.59 to 1.15) −1.17(−2.17 to −0.17) −0.59(−1.54 to 0.36) −0.54(−1.67 to 0.60) −1.86(−4.13 to 0.41) −0.16(−1.12 to 0.80) −0.28(−0.81 to 0.25) −0.38(−0.98 to 0.23)

Model 1: not adjusted for any other variables. Model 2: adjusted for maternal age, body mass index, systolic blood pressure, parity, per capital family income, education attainment, current smoker before/during pregnancy, alcohol drinker before/during pregnancy, weight gain to glucose challenge test and gestational diabetes.

Table 3 The association of maternal age ≥30 versus <30 year with domain and summary scores of quality of life. Dependent variable Physical functioning Role physical Bodily pain General health Vitality Social functioning Role emotional Mental health Physical component summary Mental component summary

Model 1 ␤ (95%CI) −1.38(−2.01 to −0.76) −3.46(−4.92 to −2.00) −0.42(−0.92 to 0.09) −0.83(−1.41 to −0.25) 0.25(−0.31 to 0.80) −1.18(−1.83 to −0.52) −0.43(−1.74 to 0.89) −0.50(−1.06 to 0.05) −0.88(−1.19 to −0.58) −0.11(−0.46 to 0.24)

Model 2 ␤ (95%CI) −1.38(−2.04 to −0.72) −4.23(−5.77 to −2.69) −0.41(−0.94 to 0.12) −0.79(−1.40 to −0.18) −0.05(−0.63 to 0.54) −1.15(−1.84 to −0.45) −0.91(−2.29 to 0.48) −0.60(−1.18 to −0.01) −0.91(−1.24 to −0.59) −0.23(−0.60 to 0.14)

Model 1: not adjusted for any other variables. Model 2: adjusted for maternal age, body mass index, systolic blood pressure, parity, per capital family income, education attainment, current smoker before/during pregnancy, alcohol drinker before/during pregnancy, weight gain to glucose challenge test and gestational diabetes.

Table 4 Multivariable subgroup analysis of gestational diabetes mellitus for domain and summary scores of quality of life, stratified by maternal age. Dependent variable Physical functioning Role physical Bodily pain General health Vitality Social functioning Role emotional Mental health Physical component summary Mental component summary

≥30 yearsa ␤ (95%CI) −0.55(−2.43 to 1.33) −1.79(−6.25 to 2.68) 0.22(−1.30 to 1.74) −2.17(−3.94 to −0.40) −0.74(−2.36 to 0.88) −0.49(−2.54 to 1.56) 0.04(−3.98 to 4.06) 0.61(−1.06 to 2.27) −0.71(−1.64 to 0.23) 0.09(−0.99 to 1.17)

<30 yearsa ␤ (95%CI) −0.1(−1.50 to 1.13) −1.5(−4.62 to 1.49) 0.27(−0.79 to 1.34) −0.69(−1.91 to 0.52) −0.53(−1.7 to 0.6) −0.56(−1.9 to 0.8) −2.97(−5.72 to −0.21) −0.56(−1.74 to 0.61) −0.06(−0.71 to 0.5) −0.6(−1.37 to 0.10)

a Adjusted for body mass index, systolic blood pressure, parity, per capital family income, education attainment, current smoker before/during pregnancy, alcohol drinker before/during pregnancy, weight gain to glucose challenge test and gestational diabetes.

4. Discussion In this large population-based cross-sectional study, we found that (1) GDM and advance maternal age were associated with reducing GH; (2) presence of maternal age ≥30 years markedly increased the effect of GDM on GH. There are a few studies that had explored the association between GDM and QoL in different countries and their findings were inconsistent. A small cross-sectional study of Irish pregnant women showed that women with GDM had slightly poor QoL than women with normal glucose tolerance. However, the difference was not statistically significant after adjustment for clinical covariates. Indeed, GDM or its diagnosis per se did not have a large effect on QoL in 2–5 years after the index pregnancy [27]. On the contrary, a study from Italy found that pregnant women with GDM in the late trimester of pregnancy had a poor GH but a better PCS than their non-GDM counterparts [16], suggesting that being labelled as GDM, and subsequent intentional and unintentional intervention

had a major confounding effect on the detected association. Being labelled as GDM also have short- and long-term impacts on their morbidities [16], which can, in turn, result in lower QoL in these pregnant women. Indeed, it is uncertain whether decreased GH in GDM women stemmed from GDM or from being labelled as GDM. In this regard, we found that GDM but not being labelled as GDM was associated with decreased GH. We also found that maternal age ≥30 years was associated with decreased QoL, which was consistent with findings from other studies. For example, a German study reported that older age was negatively associated with health-related QoL in general population [28]. Similarly, a cohort study in US suggested that advanced age was strongly associated with impaired physical function among patients with type 2 diabetes [29]. In addition, a cross-sectional study also reported that maternal age was a predictor of poor QoL among pregnant women [30]. The underlying mechanisms responsible for reduced QoL among pregnant women with advanced maternal age are not completely clear. It is presumable that peo-

Please cite this article in press as: J. Liu, et al., Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.12.004

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ple inevitably become frail with advancing age, especially when facing various challenges. Pregnancy is a physiological challenge to women. It is plausible that being pregnant at an older age may result in decreased GH. Our study has clinical and public health implications. There is a long debate regarding universal screening, diagnosis and management of GDM. Some authors are even skeptical of whether GDM is a real clinical entity [31,32]. We and others have previously demonstrated that women with GDM were at increased risks of perinatal morbidities [33,34] and increased risk of long-term risk of diabetes and cardiovascular disease [35,36]. In addition to medical outcomes, this analysis further showed that women with GDM, especially among older women, had poorer GH than women without GDM, supporting the notion that GDM is a clinical entity. In the past three decades, China has experienced a profound economic and social transition. During this period, the mean age at pregnancy has experienced a rapid increase. In this regard, mean age of pregnant women in urban Tianjin had increased from 26.3 years in 1999 to 28.3 years in 2010–2012 [2]. Recently, China has changed “One Child Policy” to “Two Children Policy” allowing a couple to have two children. With more and more couples choose to have two children, the percentage of women aged 30 years and above is expected to rise rapidly in years to come, leading to further increased risk of GDM. The increased numbers of women with GDM and aged ≥30 years are expected to lead to a further decreased GH in pregnant women in China at large. Lifestyle intervention in early pregnancy can reduce the risk of GDM and presumably, these intervention measures can also improve QoL in pregnant women although further randomized trials are warranted to test the hypothesis [37]. The study had strengths. First, our study was a large populationbased study and the findings are applicable to, at least, pregnant women residing in urban Tianjin. Second, pregnant women in this study did not know their diagnosis of GDM at the time filling in the SF-36 questionnaire. Thus, confounding by GDM diagnosis and management of GDM was unlikely. The study also had limitations. First, this study was cross-sectional in nature and the association of GDM with QoL may not be causal. Second, all participants came from urban Tianjin, a large metropolitan city in the North China and is not representative of all the pregnant women population in China. Further studies in other populations in China and other parts of the world are warranted to confirm our findings. Third, a high proportion of pregnant women were excluded in this analysis because of missing QoL or other key variables. The excluded preg-

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nant women were older, had a higher pre-pregnancy BMI and more likely to be multiparous and to have had GDM than those included (Table A1). Thus, the included women in this analysis had a lower risk of low GH. The estimated association may underestimate the true effects of GDM and older age on decreased GH. In conclusion, we demonstrated that advanced maternal age and GDM were associated with low GH, and presence of maternal age ≥30 years markedly increased the effect of GDM on GH. Given the increasing maternal age at pregnancy and increasing risk of GDM in Chinese pregnant women, decreased QoL in Chinese pregnant women may be one of major public health problems in the future. Lifestyle intervention aiming at prevention of GDM should include QoL as one of the outcomes in future studies. Funding This study was funded by the National Key Research and Development Program of China (grant number: 2018YFC1313900; 2018YFC1313903) and Nature Science Foundation of China (grant number: 81870549). Conflict of interest The authors declare that they have no conflict of interest. Acknowledgments The authors would like to express our special thanks to obstetricians and other health professionals in the 64 primary care hospitals and 6 district women and children’s health care institutes who were involved in the setting up of the cohort. X.Y. conceived and designed the study. J.L. analyzed the data and wrote the first draft. S.W., Jun.L., Jin.L. and C.Z. provided the study material and patients, collected and assembly the data; All other authors gave critical comments on the manuscript; X.Y. (the corresponding authors) and J.L. (the first author) take full responsibility for the work as a whole, including the study design, access to data, and the decision to submit and publish the manuscript. Appendix A

Table A1 Clinical and biochemical characteristics of participants according to inclusion or exclusion in analysis.

N Maternal age, years Pre-pregnancy BMI, kg/m2 Gestational age at first antenatal care visit, week Systolic blood pressure, mmHg Diastolic blood pressure, mmHg Education attainment ≥12 year Parity ≥1 Per capita family income Low Low-middle Middle-high High Current smoker before pregnancy Current smoker during pregnancy Alcohol drinker before pregnancy Alcohol drinker during pregnancy Gestational diabetes mellitus

Excluded

Include

8,944 28.6±3.1 22.5±3.5 10.5±2.7 106.1±10.6 68.6±7.7 7,300(81.88) 407(4.55)

13,358 28.5±2.9 22.3±3.4 10.4±2.3 105.3±10.8 68.3±7.8 11,041(82.67) 499(3.74)

2,811(31.43) 1,107(12.38) 3,730(41.70) 1,296(14.49) 285(3.19) 60(0.67) 2,511(28.07) 35(0.39) 570(8.37)

3,545(26.54) 1,911(14.31) 6,009(44.98) 1,893(14.17) 435(3.26) 119(0.89) 4,172(31.23) 138(1.03) 969(7.25)

P value <0.0001* 0.0042* 0.0155* <0.0001* 0.0061* 0.1303** 0.0025** <0.0001**

0.7720** 0.0711** <0.0001** <0.0001** 0.0047**

Data are reported in mean ± SD or frequency (%) or median (25th–75th percentile). * Derived from unpaired Student’s t-test. ** Derived from Chi-square test.

Please cite this article in press as: J. Liu, et al., Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.12.004

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Please cite this article in press as: J. Liu, et al., Impacts of gestational diabetes on quality of life in Chinese pregnant women in urban Tianjin, China, Prim. Care Diab. (2019), https://doi.org/10.1016/j.pcd.2019.12.004