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Original Article
Metabolic Risk/Epidemiology The Impact of Obesity on the Association between Parity and Risk of Type 2 Diabetes Mellitus
Yuki Gen1orcid, Kyuho Kim2, Joonyub Lee3, Junyoung Jung2, Sang-Hyuk Jung4, Hong-Hee Won5,6, Dokyoon Kim4,7, Yun-Sung Jo1, Yu-Bae Ahn2, Seung-Hyun Ko2, Jae-Seung Yun2orcidcorresp_icon

DOI: https://doi.org/10.4093/dmj.2024.0536
Published online: February 14, 2025
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1Department of Obstetrics and Gynecology, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

2Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

3Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea

4Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

5Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea

6Samsung Genome Institute, Samsung Medical Center, Seoul, Korea

7Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

corresp_icon Corresponding author: Jae-Seung Yun orcid Department of Internal Medicine, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, 93 Jungbu-daero, Paldal-gu, Suwon 16247, Korea E-mail: dryun@catholic.ac.kr
• Received: September 5, 2024   • Accepted: November 15, 2024

Copyright © 2025 Korean Diabetes Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

  • Background
    Most studies focus solely on the relationship between parity and type 2 diabetes mellitus (T2DM) risk, providing limited insights into other contributing or protective factors. This study aims to explore the complex relationship between parity and T2DM risk, considering additional factors such as obesity, race, and body composition.
  • Methods
    This prospective cohort study used data from 242,159 women aged 40 to 69 from the UK Biobank, none of whom had T2DM at baseline. Multivariable Cox proportional hazard models were applied to assess the association between parity and T2DM. Subgroup analyses were performed based on body mass index (BMI), waist circumference (WC), and race.
  • Results
    The hazard ratio for T2DM per additional child was 1.16 (95% confidence interval, 1.13 to 1.16). Subgroup analysis revealed that Asian women and those with obesity or abdominal obesity had a higher risk of T2DM associated with multiparity. No increased risk was observed in women with normal BMI or WC. Mediation analysis showed that WC and BMI significantly mediated the parity-T2DM relationship, accounting for 49% and 38% of the effect, respectively.
  • Conclusion
    There is a clear positive association between multiparity and T2DM risk, particularly in Asian women and those with obesity. Maintaining normal BMI and WC appears to mitigate this risk, highlighting the importance of weight management for women at higher parity levels. These findings offer crucial insights for public health interventions aimed at reducing T2DM risk among women.
• High parity raises T2DM risk after adjusting for lifestyle and socioeconomic factors.
• The risk is higher in Asian, obese, and abdominally obese women.
• Visceral adiposity, socioeconomic factors, and metainflammation are key mediators.
• Women with normal BMI and waist circumference show no increase in T2DM risk.
Type 2 diabetes mellitus (T2DM) is a prevalent global disease and a significant cause of morbidity and mortality. According to the 2021 Diabetes Atlas released by the International Diabetes Federation (IDF), approximately 530 million adults between the ages of 20 and 79 currently live with diabetes worldwide, with an estimated 17 million more cases in men than women [1]. However, it has been reported that compared to men, women have higher incidences of diabetes-related mortality and complications such as hypertension, dyslipidemia, and cardiovascular diseases [2]. In addition, most of these complications tend to have a worse prognosis in women than men; thus, healthcare professionals need to pay special attention to preventing and managing diabetes in women.
Women experience hormonal fluctuations throughout their lives, such as during menstruation, pregnancy, childbirth, and menopause; these fluctuations can potentially influence the onset of diabetes and complications [3]. During pregnancy, physiological increases occur in insulin secretion and resistance [4]. Pregnancy is also a period of rapid fat accumulation, which can significantly impact the development of postpartum diabetes and metabolic syndrome [5]. The association between parity and T2DM was first reported in the 1950s [6] and has been of research interest ever since. Most studies have reported a positive association, but some have claimed no association or even suggested a protective effect of childbirth, causing confusion and controversy [7]. Notably, most studies have focused solely on the relationship of parity with T2DM risk and need more adjustment for factors such as lifestyle habits, socio-economic status (SES), and genetic factors associated with T2DM [8]. Furthermore, to the best of our knowledge, no studies have examined whether the association between T2DM and parity is influenced by race or body composition, as well as whether there are any protective factors that could mitigate this risk relationship. Therefore, in this study, we used the large-scale UK Biobank cohort to more comprehensively investigate the relationship between parity and T2DM risk, including various considerations such as race, SES, body composition, and genetic factors. We further conducted mediation analysis on metabolic, inflammatory, and socio-economic variables to identify potential mediators in the parity-T2DM risk relationship.
Study population and study design
This study is based on data from the UK Biobank, a large prospective cohort of over 500,000 individuals from diverse racial backgrounds aged between 40 and 69 at baseline, registered between 2006 and 2010 in England, Wales, and Scotland. A wide range of information, including lifestyle habits, environment, reproductive factors, medical history, and blood, urine, and saliva samples, were obtained for further analysis [9]. The UK Biobank received ethical approval from the National Research Ethics Committee, and all participants provided written informed consent [10]. For this study, we utilized data from female participants (n=242,159) in the UK Biobank who met the following criteria: had available information on blood glucose levels and birth history and had not received a prior diagnosis of diabetes. We excluded participants who were diagnosed with T2DM at the time of registration (n=21,533), those missing parity data (n=511), and those missing the blood test data necessary for diagnosing T2DM (n=40,531).
Variable measurement
Information on lifestyle habits and medical history was collected at the time of registration in the UK Biobank through self-administered touchscreen questionnaires and personal interviews. During the interviews, trained staff measured participant height, weight, and waist circumference (WC) using standardized procedures [11]. Smoking status was categorized into nonsmoker, former, and current smoker. Physical activity was divided into two groups: those who reported engaging in moderate activity for at least 5 days a week, which were classified as exercising, and those who did not meet this criterion, which were considered not exercising. Eating habits were defined based on the recommendations for cardiometabolic health [12]. A diet pattern was considered poor if participants did not fulfill more than half of the recommendations assessed by a food frequency questionnaire. Body mass index (BMI) was calculated as weight in kilograms divided by height in square meters (kg/m²). Obesity and abdominal obesity status were based on World Health Organization classifications and the IDF consensus report, and race-specific criteria were applied to reduce errors that may arise due to racial differences (Supplementary Table 1) [13,14]. The Townsend deprivation index (TDI) measures material deprivation and socio-economic disadvantage based on four factors: unemployment rate, car ownership rate, homeownership rate, and household overcrowding. A higher score indicates a greater level of deprivation [11]. Income was obtained through a questionnaire that assessed pre-tax income and was categorized into five groups: <£18,000, £18,000–£30,999, £31,000–£51,999, £52,000–£100,000, and ≥£100,000 [15]. Depressive symptoms were assessed using a self-reported frequency of depressive mood over the past 2 weeks, and participants were categorized on frequency from low to very high [16]. Detailed descriptions of the biological sampling procedures can be found in previous publications or papers related to this study [17].
Polygenic risk scores
To generate a polygenic risk score (PRS) for T2DM, we utilized a Bayesian polygenic prediction method, PRS-continuous shrinkage [18], which infers the posterior mean effect size of each variant using linkage disequilibrium (LD) reference panel and genome-wide association study summary statistics. Here, the 1000 Genomes Project phase 3 data was used as the external LD reference panel, and summary statistics derived from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium [19] were used to infer the posterior single nucleotide polymorphism effect sizes. Individual PRSs were computed from beta coefficients as the weighted sum of the risk alleles by applying PLINK version 1.90 (https://zzz.bwh.harvard.edu/plink) [20]. More details are given in the Supplementary Methods.
Reproductive factors
Female participants provided self-reported responses regarding reproductive factors through a questionnaire. This questionnaire included age at menarche, parity, age at first childbirth, history of hysterectomy, menopausal status, and use of hormone replacement therapy [21]. Parity was categorized into six groups: nulliparous, 1, 2, 3, 4, and 5 or more childbirths.
Definition of T2DM and gestational diabetes
We utilized the International Classification of Diseases 10th Revision (ICD-10) and self-reported information to determine whether individuals had T2DM at enrollment. The occurrence of newly diagnosed T2DM during the follow-up period was identified through hospitalization records, which were coded using ICD-10 diagnostic codes. Information regarding gestational diabetes mellitus (GDM) was obtained using the same methodology (Supplementary Table 2).
Statistical analyses
We performed t-tests and chi-square tests to examine the differences in baseline characteristics between groups. Continuous variables are reported as mean±standard deviation and categorical variables as percentages. Parity was analyzed as both a continuous and a categorical variable. Multivariable adjusted Cox proportional hazard regression models were constructed to assess the association between parity and the risk of developing T2DM; the results are reported as hazard ratio (HR) with 95% confidence interval (CI). To account for potential confounders or mediators, the Cox models were stratified by age, sex, race, BMI, WC, and SES.
We further conducted a causal mediation analysis using the R package mediation to assess the mediating effects of metabolic and inflammatory factors, including WC, BMI, triglyceride, glycosylated hemoglobin (HbA1c), high-sensitivity C-reactive protein (hs-CRP), TDI, depressive symptoms, and income level. We employed a quasi-Bayesian method with 1,000 simulations to estimate variance for each analysis. Statistical analyses were conducted using PLINK version 1.9 and R version 3.9.0 (R Foundation for Statistical Computing, Vienna, Austria). The significance level was set at P<0.05, with values below that threshold considered statistically significant.
Ethics statement
The UK Biobank has received ethical approval from the North West - Haydock Research Ethics Committee (REC Reference: 21/NW/0157, IRAS Project ID: 299116), and all participants provided written informed consent.
Baseline characteristics of the study population
Our study included 242,159 female participants without a history of T2DM, stratified into six groups according to parity. The baseline clinical characteristics are shown in Table 1. The mean age of participants was 56.8 years, 60.4% were postmenopausal, and 95.4% were Caucasian. The mean BMI was 26.8 kg/m2, mean WC was 84.1 cm and mean HbA1c level was 5.4%. Only 8.9% of participants were current smokers, and 67.3% engaged in moderate to vigorous physical activity. Regarding economic status, 24.1% were in the low-income group, with an annual income below £18,000.
Among the participant population, 45,568 (18.8%) had no history of childbirth, while 106,624 (44.0%) had borne two children, the largest parity group. Only 3,612 (1.5%) had given birth to five or more children. As the number of children increased, both the average age and the proportion of non-Caucasian participants also increased; moreover, other variables such as WC, body fat, BMI, blood pressure, HbA1c, blood glucose level, and the number of applicable metabolic syndrome criteria also increased. The TDI, income, and depressive mood all showed a J-shaped pattern, with the most favorable outcomes observed in women with two children and deteriorating patterns in those with five or more children.
Association between parity and risk of T2DM
During a median follow-up period of 8.9 years, 4,045 (1.7%) participants were newly diagnosed with T2DM. Cox regression analysis, summarized in Table 2, revealed the absolute risk rate to be highest in women with five or more children, at 5.03 per 1,000 person-years (95% CI, 4.28 to 5.87), and lowest in nulliparous women, at 1.61 per 1,000 person-years (95% CI, 1.48 to 1.73). Regarding relative risk, each additional child showed a 16% increased risk of T2DM occurrence. However, the risk was not increased in women with two children compared to those with no history of childbirth. Adjusting for age, physical factors, lifestyle habits, social factors, and reproductive factors attenuated the association, but there remained an increased risk of developing T2DM among multiparous women.
After excluding 315 women with a history of GDM, the risk of developing T2DM remained consistent with the previous findings. Among women who had given birth to five or more children, who exhibited the highest risk, the HR was 3.16 (95% CI, 2.66 to 3.76) (Supplementary Table 3).
Subgroup analysis and mediation analysis
Fig. 1 shows the results of the subgroup analysis investigating the relationship between parity and T2DM risk with consideration of race, BMI, and WC. Concerning race, the Asian population demonstrated the strongest association (Supplementary Table 4). Having four or more children is related to a significantly increased risk of T2DM among women with higher BMI, specifically those who were overweight or obese (Supplementary Table 5); however, no such association was observed in the standard BMI group. Meanwhile, among individuals with abdominal obesity, having four or more children was associated with a substantial increase in T2DM risk, but in the absence of abdominal obesity, no such increase was observed (Supplementary Table 6).
We conducted a mediation analysis to examine the involvement of several potential mediators. WC, BMI, HbA1c, hsCRP, triglyceride levels, depressive mood, income, and TDI were all found to significantly mediate the relationship between parity and T2DM risk, with WC having the highest mediated proportion of 49%, followed by BMI at 38% (Table 3).
Association of genetic risk, parity, and risk of T2DM
Women in each parity category were further stratified based on a PRS for T2DM into low, intermediate, and high genetic risk groups. This risk stratification analysis revealed a sequential increase in T2DM risk with increasing parity and genetic risk (Supplementary Table 7). The highest risk was identified in women who had given birth to five or more children and harbored a high genetic risk for T2DM, with a HR of 8.56. Adjusting the primary T2DM-parity association for PRS revealed that genetic factors do not influence the risk of developing T2DM based on parity (Supplementary Table 8). In other words, no significant differences were observed among the low, intermediate, and high genetic risk groups.
This large prospective study aimed to investigate the association between parity and the risk of developing T2DM in a population of 241,159 middle-aged women without a history of T2DM in the UK Biobank. It also examined the impact of race, body composition, and SES on that association. The analysis revealed that while women with two children do not exhibit an increased risk of T2DM compared to nulliparous women, as the parity level increases to three or more children, the risk of developing T2DM increases significantly. After adjusting for lifestyle, body composition, and socio-economic variables, this risk remained, though somewhat reduced. In subgroup analysis, there were prominent associations between parity and T2DM risk in Asian individuals and women with obesity and abdominal obesity; however, particularly noteworthy was multiparous women with normal BMI and WC did not show a significant increase in T2DM risk. This finding suggests that maintaining an ideal weight and WC in multiparous women may protect against the parity-T2DM risk relationship. We also discovered that genetic factors do not significantly impact the relationship. Finally, mediation analysis highlighted abdominal obesity as a prominent mediator of the association between parity and T2DM.
Women undergo various physiological changes during pregnancy and childbirth, including increased insulin resistance, dyslipidemia, fat accumulation, hormonal fluctuations, inflammatory responses [22]. High parity may result in repeated exposure to pregnancy-related insulin resistance, which exacerbates β-cell exhaustion and thereby increases the risk of T2DM [23]. Along with the increasing insulin resistance during pregnancy, changes in lipid metabolism play a crucial role in postpartum weight retention and glucose metabolism. Specialists anticipate that prolonged or repeated exposure to pregnancy-related weight changes could have long-term negative effects on lipid metabolism [8]. Hypertriglyceridemia, which can worsen during pregnancy, facilitates fat accumulation in muscle, pancreas, and liver, thereby impairing insulin action [24]. High-density lipoprotein cholesterol (HDL-C) levels increase during mid-pregnancy but decline below the reference range in the third trimester, and these lower levels tend to persist postpartum [4]. Low HDL-C levels also contribute to increased inflammation and oxidative stress, further exacerbating insulin resistance. Studies in mouse models have reported an association between multiparity and increased maternal obesity risk [25], and several human studies have also found positive associations between multiparity, weight gain, BMI, and WC [26,27]. In general, postpartum weight retention of approximately 1.5 to 3.0 kg has been observed after 12 months of follow-up [28].
Regarding other metabolic changes, one study reported that women who have experienced pregnancy and childbirth show approximately 22% lower estrogen levels than those who have not. Decreased estrogen exposure can contribute to disorders in lipid metabolism, hyperglycemia, and the development of metabolic syndrome [29]. Additionally, pregnancy induces a systemic inflammatory state, with increased levels of pro- and anti-inflammatory cytokines such as interferon-γ and tumor necrosis factor-α [30]. Studies examining parity and inflammation have also found that women with a history of childbirth, regardless of the number of births, have increased levels of hs-CRP; this increased systemic inflammatory response also contributes to insulin resistance [31]. Finally, a meta-analysis of 62,095 women in 15 observational studies investigating parity and metabolic syndrome determined that greater parity increases the risk of developing metabolic syndrome [29].
T2DM results from insulin resistance, leading to increased insulin secretion from the pancreatic β-cells and eventual dysfunction. During a normal pregnancy, insulin resistance occurs as a physiological change in the maternal body that ensures a sustained supply of glucose to the fetus by maintaining higher blood glucose levels in the maternal circulation [19]. Given this, the relationship between T2DM and parity has been the subject of numerous studies, but their findings have not been consistent. Several studies on Asian populations have demonstrated an association between parity and T2DM risk [6]. A prospective cohort study based on the Singapore Chinese Health Study found that even after adjusting for lifestyle, reproductive factors, health factors, BMI, and other variables, women with a history of childbirth had a significantly increased risk of T2DM compared to nulliparous women. The study also found a positive association between parity and HbA1c level, regardless of T2DM diagnosis. Another study that utilized computed tomography to assess visceral fat and considered family history and other diabetes risk factors found that women with six or more children had an increased risk of developing T2DM independent of other factors [32]. Finally, a meta-analysis of eight cohort studies found that parity is an independent risk factor for T2DM, with a dose-response relationship [30]. On the other side of the argument, a paper in 2010 suggested that the relationship between multiparity and T2DM risk may be confounded or mediated by changes in body weight and socio-demographic factors; in particular, the authors observed that multiparity does not seem to promote the development of T2DM in older women without diabetes [7]. However, we should consider that this study was primarily conducted with the elderly population with a mean age of 72.5±5.4, and the sample size was relatively small. Additionally, the study design was cross-sectional, restricting the ability to establish causal relationships.
Our present findings support a progressive increase in the risk of developing T2DM among multiparous women, consistent with most previous findings. A previous meta-analysis determined a J-shaped association between parity and T2DM, with the lowest risk in women with one child; the authors suggested this may be attributed to the fact that such women are more likely to belong to a high-income group [30]. Our findings from the UK Biobank cohort, which indicate no increased risk among women with two children, also highlight a potential influence of SES on the association of T2DM risk with parity. Specifically, mothers giving birth to two children had significantly higher income and lower TDI than other groups, including nulliparous women. Considering the potential confounding factors, further analysis is needed to fully explore the association between T2DM risk in women and parity.
Pregnant women with normal pre-pregnancy weight are recommended to gain an average of 11.5 to 16.0 kg, representing a significant and rapid increase over a short period [33]. Consequently, it is expected that each successive pregnancy will lead to increased fat accumulation in the body [34]. When weight gain during pregnancy exceeds the recommended amount, there is a higher likelihood of excess postpartum weight retention or failure to return to the pre-pregnancy weight even after 1 year [35]. In a 2023 study that followed nulliparous women aged 31 to 36 years for 6 years, women who had given birth were approximately 1 kg heavier and consumed about 833.9 kJ/day more energy while also having lower activity levels [36]. In addition, a study conducted on the Chinese population reported a positive association between parity and obesity risk. It highlighted that the correlation was more robust for central obesity than general obesity [37]. When insulin resistance occurs, the remaining calories accumulate in undesirable areas, such as visceral fat, contributing to the development of abdominal obesity [38]; thus, increased insulin resistance due to pregnancy can lead to abdominal obesity [37].
Our study identified a significant association between parity and T2DM among Asian women, which may be explained by differences in insulin secretion capacity, visceral fat accumulation, and cultural and lifestyle factors related to pregnancy and postpartum care. First, Asians are known to have a lower insulin secretion capacity compared to other ethnic groups [39]. This diminished β-cell function makes them more vulnerable to T2DM, particularly in situations of increased insulin demand, such as during and after pregnancy. Second, despite having a lower BMI compared to Western populations, Asians tend to have higher levels of visceral fat accumulation [40]. Repeated pregnancies may further promote visceral fat accumulation, thereby amplifying insulin resistance and increasing the risk of T2DM [41]. Third, lifestyle changes associated with a more Westernized diet and decreased physical activity have contributed to rising obesity rates and T2DM prevalence in Asian populations [42]. These factors may have a particularly strong impact on Asians, who have a relatively lower insulin secretion capacity, thus worsening insulin resistance and increasing the risk of T2DM after multiple pregnancies. Lastly, cultural practices surrounding postpartum care may also influence T2DM risk. Some studies suggest that Asian women are more likely to retain weight for longer periods postpartum, which can lead to increased weight gain with subsequent pregnancies, particularly visceral fat, thereby increasing the risk of T2DM [43]. Inadequate weight recovery between pregnancies may further exacerbate this risk.
In our study, women who had two children did not exhibit an increased risk of T2DM compared to nulliparous women. Several factors may contribute to this observation. Previous studies have noted an association between grand multiparity and T2DM risk [7,32], and women with a parity of 1–2 children tend to belong to relatively higher SES groups [30,44]. Consistent with these findings, our data showed that women with two children had the lowest rates of obesity and the smallest WC among all parous women. Additionally, they had higher income levels, and higher SES is associated with better access to healthcare resources, healthier dietary choices, and greater health literacy, all of which can contribute to a reduced risk of T2DM. Furthermore, this group was less likely to report depressive symptoms. Lower levels of stress and depression can lead to healthier lifestyle choices, potentially reducing the risk of T2DM [45,46]. Indeed, the mediation analysis in our study identified central obesity, general obesity, the TDI, and depression as significant mediators in the relationship between parity and T2DM risk.
The strengths of this study are its large-scale prospective design and the adjustment for confounding and mediating variables such as genetic factors, body composition, and SES to comprehensively analyze the relationship between parity and T2DM risk.
This study has several limitations to consider when interpreting the results. Firstly, lifestyle and metabolic data were obtained through self-reported questionnaires at the time of registration in the UK Biobank, potentially not capturing changes after registration, such as postpartum weight changes. Secondly, the participants in the UK Biobank tend to have higher SES and health consciousness, limiting the generalizability to the broader UK population [47]. Third, breastfeeding has been shown to have a protective effect against T2DM by improving postpartum glucose metabolism and insulin sensitivity [48,49]; however, data on breastfeeding were not available in our study, and thus could not be included in the analysis. Lastly, the focus on middle-aged individuals from tertiary hospitals may not represent younger women or patients seen in primary care centers, affecting the generalizability of the findings [50].
In conclusion, we found a positive association between parity and risk of T2DM, particularly in multiparous women. This parity-T2DM risk relationship was especially evident in the Asian population and among women with obesity and abdominal obesity. However, these association was not observed in the standard BMI and standard WC groups which means the parity-T2DM risk relationship is modifiable through weight and body shape control. Our study sheds light on the importance of weight management during and after pregnancy, offering valuable insights for policymakers and healthcare providers to create effective interventions to reduce the risk of T2DM in this particular group of women.
Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2024.0536.
Supplementary methods.
dmj-2024-0536-Supplementary-methods.pdf
Supplementary Table 1.
Definition of obesity and abdominal obesity
dmj-2024-0536-Supplementary-Table-1.pdf
Supplementary Table 2.
Detailed definitions of baseline major comorbidities and outcomes
dmj-2024-0536-Supplementary-Table-2.pdf
Supplementary Table 3.
Multivariable hazard ratio and stratified risk analysis for T2DM according to parity, excluding 315 women with gestational diabetes mellitus
dmj-2024-0536-Supplementary-Table-3.pdf
Supplementary Table 4.
Hazard ratios and 95% confidential intervals for risk analysis of outcomes categorized by parity according to race
dmj-2024-0536-Supplementary-Table-4.pdf
Supplementary Table 5.
Hazard ratios and 95% confidential intervals for risk analysis of outcomes categorized by parity according to BMI
dmj-2024-0536-Supplementary-Table-5.pdf
Supplementary Table 6.
Hazard ratios and 95% confidential intervals for risk analysis of outcomes categorized by parity according to waist circumference
dmj-2024-0536-Supplementary-Table-6.pdf
Supplementary Table 7.
Joint effect analysis for parity and polygenic risk score for T2DM
dmj-2024-0536-Supplementary-Table-7.pdf
Supplementary Table 8.
Hazard ratios and 95% confidential intervals for stratified risk analysis for T2DM according to parity and polygenic risk score
dmj-2024-0536-Supplementary-Table-8.pdf

CONFLICTS OF INTEREST

Seung-Hyun Ko has been executive editor of the Diabetes & Metabolism Journal since 2022. She was not involved in the review process of this article. Otherwise, there was no conflict of interest.

AUTHOR CONTRIBUTIONS

Conception or design: J.S.Y.

Acquisition, analysis, or interpretation of data: Y.G., S.H.J., J.S.Y.

Drafting the work or revising: all authors.

Final approval of the manuscript: all authors.

FUNDING

This research was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (grant number: 2022R1F1A1072279). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgements
The authors thank the Biobank participants involved. This research using the UK Biobank Resource was approved under Application Number 67855.
Fig. 1.
Forest plots for subgroup analyses of type 2 diabetes mellitus risk according to parity: (A) race, (B) obesity, and (C) abdominal obesity.
dmj-2024-0536f1.jpg
dmj-2024-0536f2.jpg
Table 1.
Baseline characteristics of the study population
No. of live births Total (n=242,159) 0 (n=45,568) 1 (n=32,321) 2 (n=106,624) 3 (n=42,831) 4 (n=11,203) 5 or more (n=3,612) P for trends
Age, yr 56.8±8.0 54.3±8.1 55.6±8.1 57.4±7.8 58.1±7.8 58.8±7.8 58.9±8.0 <0.001
Race <0.001
 White 230,498 (95.4) 43,334 (95.4) 30,407 (94.4) 102,773 (96.6) 40,616 (95.1) 10,303 (92.2) 3,065 (85.2)
 Asian 4,292 (1.8) 690 (1.5) 625 (1.9) 1,624 (1.5) 873 (2.0) 318 (2.8) 162 (4.5)
 Black 3,117 (1.3) 568 (1.3) 570 (1.8) 817 (0.8) 605 (1.4) 316 (2.8) 241 (6.7)
 Mixed 1,564 (0.6) 395 (0.9) 273 (0.8) 533 (0.5) 233 (0.5) 85 (0.8) 45 (1.3)
 Others 2,052 (0.8) 432 (1.0) 343 (1.1) 653 (0.6) 388 (0.9) 151 (1.4) 85 (2.4)
SBP, mm Hg 137.0±20.3 134.7±19.7 135.9±20.2 137.7±20.3 138.0±20.3 138.5±20.6 139.0±20.5 <0.001
DBP, mm Hg 80.7±10.6 80.5±10.7 80.6±10.6 80.7±10.5 80.8±10.5 80.9±10.7 81.3±10.8 <0.001
BMI, kg/m2 26.8±5.0 26.5±5.4 26.9±5.1 26.7±4.7 27.1±4.9 27.7±5.2 28.7±5.6 <0.001
Waist circumference, cm 84.1±12.1 83.0±12.8 84.0±12.3 83.8±11.6 84.9±11.9 86.7±12.5 89.1±13.2 <0.001
Obesity 52,903 (21.9) 9,570 (21.1) 7,270 (22.6) 21,897 (20.6) 9,802 (23.0) 3,104 (27.8) 1,260 (35.1) <0.001
Current smoking 21,562 (8.9) 4,378 (9.6) 3,685 (11.4) 8,038 (7.5) 3,732 (8.7) 1,180 (10.5) 549 (15.2) <0.001
Physical inactivity 24,664 (10.7) 4,909 (11.1) 3,667 (11.9) 10,512 (10.3) 4,089 (10.0) 1,102 (10.5) 385 (11.7) <0.001
Poor eating habits 22,267 (9.2) 4,880 (10.7) 3,056 (9.5) 9,046 (8.5) 3,876 (9.0) 1,048 (9.4) 361 (10.0) <0.001
Townsend deprivation index –1.4±3.0 –0.8±3.2 –1.1±3.1 –1.9±2.8 –1.5±3.0 –0.7±3.3 0.5±3.6 <0.001
Income level <0.001
 Less than 18,000£ 48,248 (24.1) 8,541 (21.8) 6,918 (25.4) 19,490 (22.3) 8,999 (25.8) 3,028 (34.0) 1,272 (45.4)
 18,000 to 30,999£ 52,695 (26.3) 10,629 (27.1) 7,151 (26.3) 23,141 (26.4) 8,846 (25.4) 2,228 (25.0) 700 (25.0)
 31,000 to 51,999£ 51,578 (25.7) 10,623 (27.1) 7,157 (26.3) 22,985 (26.2) 8,504 (24.4) 1,851 (20.8) 458 (16.3)
 52,000 to 100,000£ 38,139 (19.0) 7,605 (19.4) 4,925 (18.1) 17,467 (19.9) 6,503 (18.6) 1,361 (15.3) 278 (9.9)
 Greater than 100,000£ 9,934 (5.0) 1,839 (4.7) 1,048 (3.9) 4,482 (5.1) 2,037 (5.8) 434 (4.9) 94 (3.4)
Depression burden <0.001
 Low 169,988 (73.8) 31,271 (71.7) 21,929 (71.5) 76,713 (75.5) 30,367 (74.8) 7,479 (70.9) 2,229 (66.1)
 Intermediate 48,019 (20.8) 9,814 (22.5) 6,841 (22.3) 20,188 (19.9) 8,065 (19.9) 2,321 (22.0) 790 (23.4)
 High 7,583 (3.3) 1,432 (3.3) 1,176 (3.8) 2,991 (2.9) 1,359 (3.3) 443 (4.2) 182 (5.4)
 Very high 4,802 (2.1) 1,068 (2.5) 729 (2.4) 1,736 (1.7) 789 (1.9) 309 (2.9) 171 (5.1)
HbA1c, % 5.4±0.4 5.3±0.4 5.4±0.4 5.4±0.4 5.4±0.4 5.4±0.4 5.5±0.5 <0.001
eGFR, mL/min/1.73 m2 79.3±14.4 81.3±14.8 80.0±14.6 78.8±14.1 78.5±14.3 78.2±14.7 78.4±15.3 <0.001
Total cholesterol, mg/dL 228.2±42.8 225.9±42.3 226.9±42.9 229.4±42.7 229.0±43.0 228.7±44.0 224.0±43.7 <0.001
Triglyceride, mg/dL 135.5±74.2 127.9±71.8 135.0±74.2 136.2±73.4 139.2±76.3 144.1±79.3 148.1±79.9 <0.001
HDL-C, mg/dL 62.0±14.5 63.2±15.1 61.5±14.4 62.1±14.3 61.4±14.3 60.1±14.3 57.7±13.8 <0.001
LDL-C, mg/dL 141.0±33.2 138.4±32.7 140.2±33.2 141.8±33.1 141.9±33.4 142.2±34.0 140.0±33.8 <0.001
C-reactive protein, mg/dL 2.6±4.3 2.5±4.2 2.7±4.3 2.6±4.2 2.7±4.3 3.0±4.5 3.5±5.3 <0.001

Values are presented as mean±standard deviation or number (%).

SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HbA1c, glycosylated hemoglobin; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Table 2.
Hazard ratios and 95% confidential intervals for outcomes using Cox proportional regression model
No. of live births No. of events/Total no. Incidence rate, /1,000 person-yr (95% Cl) Absolute risk, % Crude
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value
0 646/45,568 1.61 (1.48–1.73) 1.42 Ref Ref Ref Ref Ref Ref Ref
1 583/32,321 2.03 (1.87–2.21) 1.80 1.27 (1.13–1.42) <0.001 1.18 (1.06–1.32) 0.003 1.15 (1.03–1.29) 0.016 1.17 (1.04–1.32) 0.009 1.17 (1.04–1.32) 0.009 1.22 (1.07–1.39) 0.002 1.20 (1.05–1.37) 0.006
2 1,543/106,624 1.63 (1.55–1.71) 1.45 1.02 (0.93–1.11) 0.755 0.90 (0.82–0.99) 0.023 0.95 (0.87–1.05) 0.322 1.02 (0.92–1.12) 0.714 1.02 (0.92–1.12) 0.713 1.05 (0.94–1.17) 0.384 1.04 (0.93–1.16) 0.459
3 803/42,831 2.11 (1.97–2.26) 1.88 1.32 (1.19–1.46) <0.001 1.11 (0.99–1.23) 0.054 1.09 (0.98–1.21) 0.121 1.10 (0.98–1.22) 0.095 1.10 (0.98–1.22) 0.095 1.14 (1.01–1.29) 0.035 1.14 (1.00–1.29) 0.042
4 310/11,203 3.12 (2.79–3.49) 2.77 1.95 (1.70–2.24) <0.001 1.53 (1.34–1.76) <0.001 1.28 (1.12–1.47) <0.001 1.22 (1.06–1.41) 0.006 1.22 (1.06–1.41) 0.006 1.26 (1.07–1.49) 0.006 1.22 (1.03–1.44) 0.023
5 or more 160/3,612 5.03 (4.28–5.87) 4.43 3.14 (2.64–3.74) <0.001 2.24 (1.88–2.67) <0.001 1.58 (1.32–1.89) <0.001 1.50 (1.25–1.81) <0.001 1.50 (1.25–1.81) <0.001 1.34 (1.07–1.67) 0.010 1.27 (1.02–1.60) 0.036

Model 1: age and race; Model 2: model 1+body mass index, waist circumference, current smoker, regular physical activity, and eating habits; Model 3: model 2+systolic blood pressure, glycosylated hemoglobin, and estimated glomerular filtration rate; Model 4: model 3+family history of type 2 diabetes mellitus; Model 5: model 4+income level and Townsend deprivation index; Model 6: model 5+hormone replacement therapy use, menopause, and menarche age.

CI, confidence interval; HR, hazard ratio.

Table 3.
Mediation analysis for the effect of parity on the risk of T2DM
Mediator ACME P value ADE P value Total effect P value Proportion mediated, % P value
Waist circumference 0.0008 <0.001 0.0008 <0.001 0.0016 <0.001 49.3 <0.001
BMI 0.0006 <0.001 0.0010 <0.001 0.0016 <0.001 38.0 <0.001
TG 0.0003 <0.001 0.0013 <0.001 0.0016 <0.001 18.8 <0.001
TDI 0.0005 <0.001 0.0032 <0.001 0.0027 <0.001 18.4 <0.001
HbA1c 0.0002 <0.001 0.0014 <0.001 0.0016 <0.001 11.2 <0.001
CRP 0.0001 <0.001 0.0001 <0.001 0.0002 <0.001 7.0 <0.001
Income 0.0020 <0.001 0.0026 <0.001 0.0028 <0.001 5.4 <0.001
Depressive mood 0.0001 <0.001 0.0015 <0.001 0.0015 <0.001 2.9 <0.001

We utilized a quasi-Bayesian method with 1,000 simulations to estimate the variance.

T2DM, type 2 diabetes mellitus; ACME, average causal mediation effect; ADE, average direct effect; BMI, body mass index; TG, triglyceride; TDI, Townsend deprivation index; HbA1c, glycosylated hemoglobin; CRP, C-reactive protein.

  • 1. International Diabetes Federation. IDF Diabetes Atlas. 10th ed. Brussels: IDF; 2021.
  • 2. de Jong M, Woodward M, Peters SA. Diabetes, glycated hemoglobin, and the risk of myocardial infarction in women and men: a prospective cohort study of the UK biobank. Diabetes Care 2020;43:2050-9.ArticlePubMedPDF
  • 3. Vryonidou A, Paschou SA, Muscogiuri G, Orio F, Goulis DG. Mechanisms in endocrinology: metabolic syndrome through the female life cycle. Eur J Endocrinol 2015;173:R153-63.ArticlePubMed
  • 4. Landon MB, Galan HL, Jauniaux ERM, Driscoll DA, Berghella V, Grobman WA, et al. Gabbe’s obstetrics: normal and problem pregnancies. 8th ed. Philadelphia: Elsevier; 2020.
  • 5. Shi M, Zhou X, Zheng C, Pan Y. The association between parity and metabolic syndrome and its components in normal-weight postmenopausal women in China. BMC Endocr Disord 2021;21:8.ArticlePubMedPMCPDF
  • 6. Mueller NT, Mueller NJ, Odegaard AO, Gross MD, Koh WP, Yuan JM, et al. Higher parity is associated with an increased risk of type-II diabetes in Chinese women: the Singapore Chinese Health Study. BJOG 2013;120:1483-9.ArticlePubMedPMC
  • 7. Fowler-Brown AG, de Boer IH, Catov JM, Carnethon MR, Kamineni A, Kuller LH, et al. Parity and the association with diabetes in older women. Diabetes Care 2010;33:1778-82.ArticlePubMedPMCPDF
  • 8. Nicholson WK, Asao K, Brancati F, Coresh J, Pankow JS, Powe NR. Parity and risk of type 2 diabetes: the Atherosclerosis Risk in Communities Study. Diabetes Care 2006;29:2349-54.PubMed
  • 9. Ollier W, Sprosen T, Peakman T. UK Biobank: from concept to reality. Pharmacogenomics 2005;6:639-46.ArticlePubMed
  • 10. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med 2015;12:e1001779.ArticlePubMedPMC
  • 11. Zoet GA, Paauw ND, Groenhof K, Franx A, Gansevoort RT, Groen H, et al. Association between parity and persistent weight gain at age 40-60 years: a longitudinal prospective cohort study. BMJ Open 2019;9:e024279.ArticlePubMedPMC
  • 12. Mozaffarian D. Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: a comprehensive review. Circulation 2016;133:187-225.ArticlePubMedPMC
  • 13. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.ArticlePubMed
  • 14. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120:1640-5.ArticlePubMed
  • 15. Zhang YB, Chen C, Pan XF, Guo J, Li Y, Franco OH, et al. Associations of healthy lifestyle and socioeconomic status with mortality and incident cardiovascular disease: two prospective cohort studies. BMJ 2021;373:n604.ArticlePubMedPMC
  • 16. Lee SN, Yun JS, Ko SH, Ahn YB, Yoo KD, Her SH, et al. Impacts of gender and lifestyle on the association between depressive symptoms and cardiovascular disease risk in the UK Biobank. Sci Rep 2023;13:10758.ArticlePubMedPMCPDF
  • 17. Elliott P, Peakman TC; UK Biobank. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int J Epidemiol 2008;37:234-44.ArticlePubMed
  • 18. Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun 2019;10:1776.ArticlePubMedPMCPDF
  • 19. Scott RA, Scott LJ, Magi R, Marullo L, Gaulton KJ, Kaakinen M, et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 2017;66:2888-902.PubMedPMC
  • 20. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007;81:559-75.ArticlePubMedPMC
  • 21. Peters SA, Woodward M. Women’s reproductive factors and incident cardiovascular disease in the UK Biobank. Heart 2018;104:1069-75.ArticlePubMed
  • 22. Kaaja RJ, Greer IA. Manifestations of chronic disease during pregnancy. JAMA 2005;294:2751-7.ArticlePubMed
  • 23. Moon JH, Lee J, Kim KH, Kim HJ, Kim H, Cha HN, et al. Multiparity increases the risk of diabetes by impairing the proliferative capacity of pancreatic β cells. Exp Mol Med 2023;55:2269-80.ArticlePubMedPMCPDF
  • 24. Fang H, Li Q, Wang H, Ren Y, Zhang L, Yang L. Maternal nutrient metabolism in the liver during pregnancy. Front Endocrinol (Lausanne) 2024;15:1295677.ArticlePubMedPMC
  • 25. Rebholz SL, Jones T, Burke KT, Jaeschke A, Tso P, D’Alessio DA, et al. Multiparity leads to obesity and inflammation in mothers and obesity in male offspring. Am J Physiol Endocrinol Metab 2012;302:E449-57.ArticlePubMed
  • 26. Williamson DF, Madans J, Pamuk E, Flegal KM, Kendrick JS, Serdula MK. A prospective study of childbearing and 10-year weight gain in US white women 25 to 45 years of age. Int J Obes Relat Metab Disord 1994;18:561-9.PubMed
  • 27. Smith DE, Lewis CE, Caveny JL, Perkins LL, Burke GL, Bild DE. Longitudinal changes in adiposity associated with pregnancy. The CARDIA Study. Coronary Artery Risk Development in Young Adults Study. JAMA 1994;271:1747-51.ArticlePubMed
  • 28. Kac G, Benicio MH, Velasquez-Melendez G, Valente JG, Struchiner CJ. Gestational weight gain and prepregnancy weight influence postpartum weight retention in a cohort of brazilian women. J Nutr 2004;134:661-6.ArticlePubMed
  • 29. Sun MH, Wen ZY, Wang R, Gao C, Yin JL, Chang YJ, et al. Parity and metabolic syndrome risk: a systematic review and meta-analysis of 15 observational studies with 62,095 women. Front Med (Lausanne) 2022;9:926944.ArticlePubMedPMC
  • 30. Li P, Shan Z, Zhou L, Xie M, Bao W, Zhang Y, et al. Mechanisms in endocrinology: parity and risk of type 2 diabetes: a systematic review and dose-response meta-analysis. Eur J Endocrinol 2016;175:R231-45.ArticlePubMed
  • 31. Iversen DS, Stoy J, Kampmann U, Voss TS, Madsen LR, Moller N, et al. Parity and type 2 diabetes mellitus: a study of insulin resistance and β-cell function in women with multiple pregnancies. BMJ Open Diabetes Res Care 2016;4:e000237.ArticlePubMedPMC
  • 32. Araneta MR, Barrett-Connor E. Grand multiparity is associated with type 2 diabetes in Filipino American women, independent of visceral fat and adiponectin. Diabetes Care 2010;33:385-9.ArticlePubMedPDF
  • 33. Beyerlein A, Lack N, von Kries R. Within-population average ranges compared with Institute of Medicine recommendations for gestational weight gain. Obstet Gynecol 2010;116:1111-8.ArticlePubMed
  • 34. Hajian-Tilaki KO, Heidari B. Prevalence of obesity, central obesity and the associated factors in urban population aged 20-70 years, in the north of Iran: a population-based study and regression approach. Obes Rev 2007;8:3-10.ArticlePubMed
  • 35. Siega-Riz AM, Herring AH, Carrier K, Evenson KR, Dole N, Deierlein A. Sociodemographic, perinatal, behavioral, and psychosocial predictors of weight retention at 3 and 12 months postpartum. Obesity (Silver Spring) 2010;18:1996-2003.ArticlePubMedPDF
  • 36. Makama M, Earnest A, Lim S, Skouteris H, Hill B, Teede H, et al. Assessing patterns of change in lifestyle behaviours by parity: a longitudinal cohort study. Int J Epidemiol 2023;52:589-99.ArticlePubMedPDF
  • 37. Li W, Wang Y, Shen L, Song L, Li H, Liu B, et al. Association between parity and obesity patterns in a middle-aged and older Chinese population: a cross-sectional analysis in the TongjiDongfeng cohort study. Nutr Metab (Lond) 2016;13:72.ArticlePubMedPMCPDF
  • 38. Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest 2000;106:473-81.ArticlePubMedPMC
  • 39. Cho YM. Incretin physiology and pathophysiology from an Asian perspective. J Diabetes Investig 2015;6:495-507.PubMed
  • 40. Williams R, Periasamy M. Genetic and environmental factors contributing to visceral adiposity in Asian populations. Endocrinol Metab (Seoul) 2020;35:681-95.ArticlePubMedPMCPDF
  • 41. Gunderson EP, Sternfeld B, Wellons MF, Whitmer RA, Chiang V, Quesenberry CP Jr, et al. Childbearing may increase visceral adipose tissue independent of overall increase in body fat. Obesity (Silver Spring) 2008;16:1078-84.ArticlePubMedPMCPDF
  • 42. Yabe D, Seino Y, Fukushima M, Seino S. β Cell dysfunction versus insulin resistance in the pathogenesis of type 2 diabetes in East Asians. Curr Diab Rep 2015;15:602.ArticlePubMedPDF
  • 43. Kinnunen TI, Waage CW, Sommer C, Sletner L, Raitanen J, Jenum AK. Ethnic differences in gestational weight gain: a population-based cohort study in Norway. Matern Child Health J 2016;20:1485-96.ArticlePubMedPDF
  • 44. Naver KV, Lundbye-Christensen S, Gorst-Rasmussen A, Nilas L, Secher NJ, Rasmussen S, et al. Parity and risk of diabetes in a Danish nationwide birth cohort. Diabet Med 2011;28:43-7.ArticlePubMed
  • 45. Mezuk B, Eaton WW, Albrecht S, Golden SH. Depression and type 2 diabetes over the lifespan: a meta-analysis. Diabetes Care 2008;31:2383-90.ArticlePubMedPMCPDF
  • 46. Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med 2017;15:131.ArticlePubMedPMCPDF
  • 47. Davis KA, Coleman JR, Adams M, Allen N, Breen G, Cullen B, et al. Mental health in UK Biobank: development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open 2020;6:e18.ArticlePubMedPMC
  • 48. Horta BL, Loret de Mola C, Victora CG. Long-term consequences of breastfeeding on cholesterol, obesity, systolic blood pressure and type 2 diabetes: a systematic review and meta-analysis. Acta Paediatr 2015;104:30-7.ArticlePubMed
  • 49. Horta BL, de Lima NP. Breastfeeding and type 2 diabetes: systematic review and meta-analysis. Curr Diab Rep 2019;19:1.ArticlePubMedPDF
  • 50. Marinho FS, Moram CB, Rodrigues PC, Leite NC, Salles GF, Cardoso CR. Treatment adherence and its associated factors in patients with type 2 diabetes: results from the Rio de Janeiro type 2 diabetes cohort study. J Diabetes Res 2018;2018:8970196.ArticlePubMedPMCPDF

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      The Impact of Obesity on the Association between Parity and Risk of Type 2 Diabetes Mellitus
      Image Image
      Fig. 1. Forest plots for subgroup analyses of type 2 diabetes mellitus risk according to parity: (A) race, (B) obesity, and (C) abdominal obesity.
      Graphical abstract
      The Impact of Obesity on the Association between Parity and Risk of Type 2 Diabetes Mellitus
      No. of live births Total (n=242,159) 0 (n=45,568) 1 (n=32,321) 2 (n=106,624) 3 (n=42,831) 4 (n=11,203) 5 or more (n=3,612) P for trends
      Age, yr 56.8±8.0 54.3±8.1 55.6±8.1 57.4±7.8 58.1±7.8 58.8±7.8 58.9±8.0 <0.001
      Race <0.001
       White 230,498 (95.4) 43,334 (95.4) 30,407 (94.4) 102,773 (96.6) 40,616 (95.1) 10,303 (92.2) 3,065 (85.2)
       Asian 4,292 (1.8) 690 (1.5) 625 (1.9) 1,624 (1.5) 873 (2.0) 318 (2.8) 162 (4.5)
       Black 3,117 (1.3) 568 (1.3) 570 (1.8) 817 (0.8) 605 (1.4) 316 (2.8) 241 (6.7)
       Mixed 1,564 (0.6) 395 (0.9) 273 (0.8) 533 (0.5) 233 (0.5) 85 (0.8) 45 (1.3)
       Others 2,052 (0.8) 432 (1.0) 343 (1.1) 653 (0.6) 388 (0.9) 151 (1.4) 85 (2.4)
      SBP, mm Hg 137.0±20.3 134.7±19.7 135.9±20.2 137.7±20.3 138.0±20.3 138.5±20.6 139.0±20.5 <0.001
      DBP, mm Hg 80.7±10.6 80.5±10.7 80.6±10.6 80.7±10.5 80.8±10.5 80.9±10.7 81.3±10.8 <0.001
      BMI, kg/m2 26.8±5.0 26.5±5.4 26.9±5.1 26.7±4.7 27.1±4.9 27.7±5.2 28.7±5.6 <0.001
      Waist circumference, cm 84.1±12.1 83.0±12.8 84.0±12.3 83.8±11.6 84.9±11.9 86.7±12.5 89.1±13.2 <0.001
      Obesity 52,903 (21.9) 9,570 (21.1) 7,270 (22.6) 21,897 (20.6) 9,802 (23.0) 3,104 (27.8) 1,260 (35.1) <0.001
      Current smoking 21,562 (8.9) 4,378 (9.6) 3,685 (11.4) 8,038 (7.5) 3,732 (8.7) 1,180 (10.5) 549 (15.2) <0.001
      Physical inactivity 24,664 (10.7) 4,909 (11.1) 3,667 (11.9) 10,512 (10.3) 4,089 (10.0) 1,102 (10.5) 385 (11.7) <0.001
      Poor eating habits 22,267 (9.2) 4,880 (10.7) 3,056 (9.5) 9,046 (8.5) 3,876 (9.0) 1,048 (9.4) 361 (10.0) <0.001
      Townsend deprivation index –1.4±3.0 –0.8±3.2 –1.1±3.1 –1.9±2.8 –1.5±3.0 –0.7±3.3 0.5±3.6 <0.001
      Income level <0.001
       Less than 18,000£ 48,248 (24.1) 8,541 (21.8) 6,918 (25.4) 19,490 (22.3) 8,999 (25.8) 3,028 (34.0) 1,272 (45.4)
       18,000 to 30,999£ 52,695 (26.3) 10,629 (27.1) 7,151 (26.3) 23,141 (26.4) 8,846 (25.4) 2,228 (25.0) 700 (25.0)
       31,000 to 51,999£ 51,578 (25.7) 10,623 (27.1) 7,157 (26.3) 22,985 (26.2) 8,504 (24.4) 1,851 (20.8) 458 (16.3)
       52,000 to 100,000£ 38,139 (19.0) 7,605 (19.4) 4,925 (18.1) 17,467 (19.9) 6,503 (18.6) 1,361 (15.3) 278 (9.9)
       Greater than 100,000£ 9,934 (5.0) 1,839 (4.7) 1,048 (3.9) 4,482 (5.1) 2,037 (5.8) 434 (4.9) 94 (3.4)
      Depression burden <0.001
       Low 169,988 (73.8) 31,271 (71.7) 21,929 (71.5) 76,713 (75.5) 30,367 (74.8) 7,479 (70.9) 2,229 (66.1)
       Intermediate 48,019 (20.8) 9,814 (22.5) 6,841 (22.3) 20,188 (19.9) 8,065 (19.9) 2,321 (22.0) 790 (23.4)
       High 7,583 (3.3) 1,432 (3.3) 1,176 (3.8) 2,991 (2.9) 1,359 (3.3) 443 (4.2) 182 (5.4)
       Very high 4,802 (2.1) 1,068 (2.5) 729 (2.4) 1,736 (1.7) 789 (1.9) 309 (2.9) 171 (5.1)
      HbA1c, % 5.4±0.4 5.3±0.4 5.4±0.4 5.4±0.4 5.4±0.4 5.4±0.4 5.5±0.5 <0.001
      eGFR, mL/min/1.73 m2 79.3±14.4 81.3±14.8 80.0±14.6 78.8±14.1 78.5±14.3 78.2±14.7 78.4±15.3 <0.001
      Total cholesterol, mg/dL 228.2±42.8 225.9±42.3 226.9±42.9 229.4±42.7 229.0±43.0 228.7±44.0 224.0±43.7 <0.001
      Triglyceride, mg/dL 135.5±74.2 127.9±71.8 135.0±74.2 136.2±73.4 139.2±76.3 144.1±79.3 148.1±79.9 <0.001
      HDL-C, mg/dL 62.0±14.5 63.2±15.1 61.5±14.4 62.1±14.3 61.4±14.3 60.1±14.3 57.7±13.8 <0.001
      LDL-C, mg/dL 141.0±33.2 138.4±32.7 140.2±33.2 141.8±33.1 141.9±33.4 142.2±34.0 140.0±33.8 <0.001
      C-reactive protein, mg/dL 2.6±4.3 2.5±4.2 2.7±4.3 2.6±4.2 2.7±4.3 3.0±4.5 3.5±5.3 <0.001
      No. of live births No. of events/Total no. Incidence rate, /1,000 person-yr (95% Cl) Absolute risk, % Crude
      Model 1
      Model 2
      Model 3
      Model 4
      Model 5
      Model 6
      HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value
      0 646/45,568 1.61 (1.48–1.73) 1.42 Ref Ref Ref Ref Ref Ref Ref
      1 583/32,321 2.03 (1.87–2.21) 1.80 1.27 (1.13–1.42) <0.001 1.18 (1.06–1.32) 0.003 1.15 (1.03–1.29) 0.016 1.17 (1.04–1.32) 0.009 1.17 (1.04–1.32) 0.009 1.22 (1.07–1.39) 0.002 1.20 (1.05–1.37) 0.006
      2 1,543/106,624 1.63 (1.55–1.71) 1.45 1.02 (0.93–1.11) 0.755 0.90 (0.82–0.99) 0.023 0.95 (0.87–1.05) 0.322 1.02 (0.92–1.12) 0.714 1.02 (0.92–1.12) 0.713 1.05 (0.94–1.17) 0.384 1.04 (0.93–1.16) 0.459
      3 803/42,831 2.11 (1.97–2.26) 1.88 1.32 (1.19–1.46) <0.001 1.11 (0.99–1.23) 0.054 1.09 (0.98–1.21) 0.121 1.10 (0.98–1.22) 0.095 1.10 (0.98–1.22) 0.095 1.14 (1.01–1.29) 0.035 1.14 (1.00–1.29) 0.042
      4 310/11,203 3.12 (2.79–3.49) 2.77 1.95 (1.70–2.24) <0.001 1.53 (1.34–1.76) <0.001 1.28 (1.12–1.47) <0.001 1.22 (1.06–1.41) 0.006 1.22 (1.06–1.41) 0.006 1.26 (1.07–1.49) 0.006 1.22 (1.03–1.44) 0.023
      5 or more 160/3,612 5.03 (4.28–5.87) 4.43 3.14 (2.64–3.74) <0.001 2.24 (1.88–2.67) <0.001 1.58 (1.32–1.89) <0.001 1.50 (1.25–1.81) <0.001 1.50 (1.25–1.81) <0.001 1.34 (1.07–1.67) 0.010 1.27 (1.02–1.60) 0.036
      Mediator ACME P value ADE P value Total effect P value Proportion mediated, % P value
      Waist circumference 0.0008 <0.001 0.0008 <0.001 0.0016 <0.001 49.3 <0.001
      BMI 0.0006 <0.001 0.0010 <0.001 0.0016 <0.001 38.0 <0.001
      TG 0.0003 <0.001 0.0013 <0.001 0.0016 <0.001 18.8 <0.001
      TDI 0.0005 <0.001 0.0032 <0.001 0.0027 <0.001 18.4 <0.001
      HbA1c 0.0002 <0.001 0.0014 <0.001 0.0016 <0.001 11.2 <0.001
      CRP 0.0001 <0.001 0.0001 <0.001 0.0002 <0.001 7.0 <0.001
      Income 0.0020 <0.001 0.0026 <0.001 0.0028 <0.001 5.4 <0.001
      Depressive mood 0.0001 <0.001 0.0015 <0.001 0.0015 <0.001 2.9 <0.001
      Table 1. Baseline characteristics of the study population

      Values are presented as mean±standard deviation or number (%).

      SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HbA1c, glycosylated hemoglobin; eGFR, estimated glomerular filtration rate; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

      Table 2. Hazard ratios and 95% confidential intervals for outcomes using Cox proportional regression model

      Model 1: age and race; Model 2: model 1+body mass index, waist circumference, current smoker, regular physical activity, and eating habits; Model 3: model 2+systolic blood pressure, glycosylated hemoglobin, and estimated glomerular filtration rate; Model 4: model 3+family history of type 2 diabetes mellitus; Model 5: model 4+income level and Townsend deprivation index; Model 6: model 5+hormone replacement therapy use, menopause, and menarche age.

      CI, confidence interval; HR, hazard ratio.

      Table 3. Mediation analysis for the effect of parity on the risk of T2DM

      We utilized a quasi-Bayesian method with 1,000 simulations to estimate the variance.

      T2DM, type 2 diabetes mellitus; ACME, average causal mediation effect; ADE, average direct effect; BMI, body mass index; TG, triglyceride; TDI, Townsend deprivation index; HbA1c, glycosylated hemoglobin; CRP, C-reactive protein.

      Gen Y, Kim K, Lee J, Jung J, Jung SH, Won HH, Kim D, Jo YS, Ahn YB, Ko SH, Yun JS. The Impact of Obesity on the Association between Parity and Risk of Type 2 Diabetes Mellitus. Diabetes Metab J. 2025 Feb 14. doi: 10.4093/dmj.2024.0536. Epub ahead of print.
      Received: Sep 05, 2024; Accepted: Nov 15, 2024
      DOI: https://doi.org/10.4093/dmj.2024.0536.

      Diabetes Metab J : Diabetes & Metabolism Journal
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