Prevalence, Adverse Neonatal Outcomes, and Factors Associated With Maternal Dyslipidemia Among Women Admitted in the Postnatal Unit at Kayunga Regional Referral Hospital, Uganda

Prevalence, Adverse Neonatal Outcomes, and Factors Associated With Maternal Dyslipidemia Among Women Admitted in the Postnatal Unit at Kayunga Regional Referral Hospital, Uganda | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence, Adverse Neonatal Outcomes, and Factors Associated With Maternal Dyslipidemia Among Women Admitted in the Postnatal Unit at Kayunga Regional Referral Hospital, Uganda Fathi Abdi Farah, Sowda Abdikarim Sheikh Isse, Okurut Emmanuel, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8156962/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Introduction: Maternal dyslipidemia is an emerging public health concern associated with adverse pregnancy and neonatal outcomes, including preterm birth, macrosomia, and neonatal complications. Despite extensive global research, there is limited data from Uganda regarding the burden, determinants, and neonatal implications of maternal lipid abnormalities. Methods A cross-sectional study was conducted among 376 postpartum women at Kayunga Regional Referral Hospital from May to September 2025. Data on socio-demographic, obstetric, medical, and lifestyle characteristics were collected using structured questionnaires. Blood samples were analyzed for lipid profiles including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Neonatal outcomes were abstracted from clinical records. Bivariate and multivariate logistic regression analyses were performed to identify factors independently associated with maternal dyslipidaemia, with significance considered at p < 0.05. Results The prevalence of maternal dyslipidemia was 58.2% (219/376; 95% CI: 53.2%-63.2%). The most common lipid abnormality was low HDL-C (< 40/50 mg/dL), observed in 71.5% (269/376) of mothers, followed by elevated LDL-C (≥ 130 mg/dL) in 41.2% (155/376), hypertriglyceridemia (TG ≥ 150 mg/dL) in 39.9% (150/376), and elevated total cholesterol (TC ≥ 200 mg/dL) in 21.8% (82/376). Maternal dyslipidemia was significantly associated with preterm birth (χ² = 9.15, p = 0.0025), neonatal hypoglycemia (p = 0.018), and neonatal jaundice (χ² = 17.33, p < 0.001). Multivariate analysis revealed that pre-pregnancy obesity (AOR = 2.17, 95% CI: 1.73–6.31, p = 0.04), pre-eclampsia (AOR = 2.07, 95% CI: 1.67–6.43, p = 0.02), current smoking (AOR = 2.36, 95% CI: 1.93–4.62, p = 0.01), and physical inactivity (AOR = 2.74, 95% CI: 1.63–7.48, p = 0.04) were independent predictors of maternal dyslipidemia. Conclusion Maternal dyslipidemia is highly prevalent among postpartum women, with low HDL-C being the most frequent lipid abnormality. There is urgent need for routine lipid screening during antenatal care and implementation of lifestyle interventions to mitigate dyslipidemia and improve maternal and neonatal health outcomes. Maternal dyslipidemia Lipid profile Postpartum women Neonatal outcomes Preterm birth Uganda Cross-sectional study Figures Figure 1 Introduction Pregnancy is associated with considerable changes within the mother’s lipid profiles. Physiologic pregnancy increases levels of cholesterol, LDL, and triglycerides, peaking in late pregnancy( 1 ). Yet, maternal dyslipidemia is defined as pathological abnormalities in lipid levels (elevated levels of TC, LDL-C, and TG, and low levels of HDL-C) above the norm during pregnancy( 2 , 3 ). The role of dyslipidemia in pregnancy is being increasingly appreciated, as this becomes increasingly relevant as a pregnancy complication affecting both mother and baby. Previous studies imply associations with hyperlipidemia and pregnancy-related complications in the mother, including those of pre-eclampsia and gestational diabetes mellitus, as well as those affecting neonates, including those of preterm and macrosomic babies( 4 – 6 ). This, as evidenced by the meta-analysis conducted by Jiang et al. in 2017, which suggested significant relations of maternal pregnancy abnormalities and associations with preterm birth( 7 ). Additionally, studies conducted on large populations in China suggested associations of high triglycerides with hypertensive and large-for-gestational aged neonates( 8 ). The outcome of neonates likely to be affected by maternal lipid levels is neither limited to preterm birth, low birth weight, macrosomia, nor hypoglycemia, but also to jaundice( 9 ). There are studies, nonetheless, wherein maternal hypertriglyceridemia and high LDL-C are directly associated with overnutrition and macrosomia in infants( 10 ). Conversely, the presence of maternal dyslipidemia may also be considered as an indication of an underlining metabolic problem, for instance, insulin resistance, thereby increasing the likelihood of hypoglycemia and jaundice in newborn infants, as often manifested in infants born to diabetic women. According to Smith et al. (2018), pregnant women with dyslipidemia are more likely to have premature deliveries, suggesting thereby an association between pro-atherogenic dyslipidemia and placental insufficiency leading to premature births( 10 ). In addition, on the adverse effects of pregnancy, a cross-sectional study in Iran found pregnant women with hyperlipidemia, particularly those with high TG and cholesterol, are at greater risk for neonatal adverse outcomes, namely, neonatal ICU admission and low Apgar score( 11 ). In mechanism, maternal dyslipidemia may underlie the high oxidative and inflammation level within the intrauterine setting, thereby affecting placental and fetal development( 3 , 11 ). In spite of this, there is very limited literature from sub-Saharan Africa, including Uganda, on the childhood prevalence of maternal dyslipidemia and its neonatal outcomes( 12 ). Traditionally, in many low and middle-income countries, including those in sub-Saharan Africa, routine lipid level measurements are still not part of antenatal care, unless in situations of undernutrition and infectious disease risk in pregnancy. There is only very limited attention given to cardiovascular disease risk factors during pregnancy in resource-constrained settings, and only recently has this changed. In lactating women in Ghana, for example, dyslipidemia is known to be very high, with more than half of lactating women either having high levels of LDL and low levels of HDL( 13 ). This does, therefore, draw attention to the fact that other than cardiovascular disease, metabolic risk factors are still high in the African lactating woman’s populace. But, as per our briefing, no valid research as yet explored maternal blood lipid levels and neonates within the Ugandan hospital setting, as at the date of this study( 13 ). The Kayunga Regional Referral Hospital (KRRH) covers a vast majority of the peri-rural and rural populations in central Uganda, where the magnitude of dyslipidemia and non-communicable conditions in pregnancy is yet undetermined. Determining the magnitude of maternal dyslipidemia and the level at which it influences pregnancy is crucial for the determination of interventions and measures to address the issue. Our study hypothesized that dyslipidemia is common among women within the study setting and influences the rate of adverse neonatal outcomes. The aim of this research was, therefore, to establish the level of dyslipidemia among women in the postnatal ward within the KRRH, to provide insights into what the adverse outcomes are for neonates whose mothers are dyslipidemic, and to establish the essential maternal variables associated with this level of dyslipidemia. In doing so, this research will provide the necessary evidence to establish whether universally routine screening and prevention for dyslipidemia is required within this setting. Materials and Methods Study Design and Setting The study was a cross-sectional survey and was carried out in Kayunga Regional Referral Hospital (KRRH) in central Uganda. The hospital is a referral center and is mainly serviced by several other nearby districts, including Kayunga, Kamuli, Nakasongola, and parts thereof in Mukono. The maternity facility in the hospital handles general as well as complicated cases of obstetrics, and the institution also has a post-delivery ward where the mother is supposed to spend at least 24 to 48 hours after giving birth. The study was carried out for 4 months, ranging from May to September 2025, within which the study coincided with general provision of maternity health to women. Study Population and Sampling The proposed study will target women who have just given birth and are admitted in the postnatal ward at KRRH. Inclusion criteria were all women in the postnatal unit, whether by vaginal or Caesarean section, in the study period and those who gave informed consent to participate Exclusion criteria were women who did not give consent, and any mother known to have had pregnancy-associated lipid abnormalities, as well as those on any lipid-lowering agent. Postpartum women who were so critically ill they were not able to be interviewed and provide blood were also excluded from this study. The consecutive sampling technique was used, whereby all eligible women were recruited into the study until the required sample size was achieved. Study Sampling Sample size determination First objective (Daniel, 1999) formula was used to calculate the sample size Where: n = needed sample size estimate Z = critical value for normal distribution at 95% confidence level, corresponding to 1.96 P = estimated prevalence rate, which is 66.7% from a study done in Ethiopia ( 14 ). Hence, Added by 10% for non-responses Final Sample Size Therefore to achieve all objectives I take the maximum sample size 376 Data Collection The data were collected by the principal investigator along with the research assistant through face-to-face interviews and chart reviews in the postnatal ward, usually on the first day after delivery. The structured format of the questionnaire allowed documentation of the maternal sociodemographic factors like age, marital status, educational level, occupation, and family income, as well as pregnancy-related details like gravidity, parity, antenatal attendance, and any pregnancy-related complications. The medical history recorded included height and pre-pregnancy weight of the woman to calculate the BMI, and presence of any chronic conditions like hypertention, diabetes, gestational diabetes, and pre-eclampsia. Lifestyle-related issues like the exposure of the mother to smoking, and alcohol and her level of physicial activity, where the lack of physicial activity was described as any activity other than regular or predominantly sedentery activity. The antenatal data were abstracted from ante-natal cards or remembered by the participant. Pre-pregnancy body mass index was defined by WHO standards. The first ante-natal weight recorded was used if there was no recorded pre-pregnancy weight. Blood pressures and hospital diagnoses were used to define pre-eclampsia and Diabetes. Venous blood samples (5mL) were obtained for the determination of the lipid profile, ideally after an overnight fast, but this was not feasible in all cases, especially after partum. The specimen was processed within an hour, and the analysis was done on an automated analyzer (Cobas 6000). The lipids assessed were TC, LDL-C, HDL-C, and TG. Laboratory abnormalities were compared to standard thresholds, and dyslipidemia was defined as the presence of any of the abnormalities. Neonatal information was collected through maternity and newborn files, which included gestational age, birth weight, Apgar score, newborn complications, and NICU admissions. The primary outcomes were preterm birth, low birth weight, macrosomia, neonatal hypoglycemia, jaundice, and early neonatal mortality. The above-mentioned outcomes were compared between women with and those without dyslipidemia. Statistical Analysis Data was entered into Microsoft Excel 2016 and processed with the aid of IBM SPSS software, version 20, after thorough cleaning for completeness and accuracy. The data was described using statistics, with categorical variables described in frequencies and percentages, while continuous variables were described by means and standard deviations or median and interquartile range, as appropriate. The prevalence of dyslipidemia in mothers and various lipid abnormalities and their association with neonatal outcomes was determined using 95% confidence intervals. Comparison of categorical variables was conducted by Pearson’s chi-square test or Exact Test, while odds ratio with 95% confidence interval determined associations. Factors associated with maternal dyslipidemia were assessed initially by performing bivariate logistic regression analysis. The variables were considered for multivariate logistic regression analysis, and backward stepwise procedure, if p < 0.20. Predictors were declared significant for the model if p was less than 0.05, and the adjusted odds ratios with 95% confidence interval were used. The model’s goodness of fit was tested with Hosmer & Lemeshow’s test, and the level of significance was set at p < 0.05. Ethical consideration The research was granted ethical clearance by the Kampala International University Ethics Committee, and it also received approval from the Kayunga Regional Referral Hospital and the Uganda National Council for Science and Technology. The research was conducted on volunteers who were freely informed about the research objectives and procedures and were required to provide their voluntary and written-informed consent. The research ensured the participants’ privacy by assigning them codes, as opposed to names, and did not carry any financial rewards, but they were, however, granted free lifestyle and lipid tests. The research was governed by all applicable human research standards Results Participant Characteristics A total of 376 postpartum women were recruited, with an overall participation rate of over 95%. Tables 1 and 2 describe the participants' socio-demographic profiles. The median age was 29 (range 16–44) years. The majority were married (75.8%) and educated, though 23.4% never attended school. Unemployment was high (41.8%); the remainder were self-employed or formally employed. The parity level was high, with 53.5% and 25.8% giving 3–5 and greater than five previous pregnancies, respectively, and also reflective of 25.3% with more than five total live births (Table 2 ). Pregnancy-related complications were gestational diabetes (26.9%) and preeclampsia (23.1%). The lifestyle attributes defined low rates of current smoking (3.2%); nonetheless, 33.8% drank either during pregnancy or PPD. The majority, 72.3%, did no special diet, centered on high-carbohydrate traditional foods. Physical inactivity was apparent, as 34.3% did no exercises, and only 45.2% exercised regularly. Family history of cardiovascular disease was yes in 24.2%. Additionally, 23.7% and 17.6% tested high for blood pressure and have been told they have been told they have diabetes, respectively. Table 1 Socio-demographic characteristics of study participants Caption: Socio-demographic characteristics of postpartum women enrolled at Kayunga Regional Referral Hospital (KRRH). Frequencies and percentages are used to display values. Variable Frequencies (N = 376) Percentages (%) Age (years) < 20 94 25.0 20–29 95 25.3 30–39 106 28.2 40+ 81 21.5 Marital status Single 91 24.2 Married 285 75.8 Education level No education 88 23.4 Primary 109 29.0 Secondary 108 28.7 Tertiary 71 18.9 Occupation Unemployed 157 41.8 Self-employed 140 37.2 Formal employment 79 21.0 Monthly household income 1,000,000/- 81 21.5 Table 2 Maternal health and lifestyle characteristics. Caption: Maternal clinical and lifestyle characteristics of postpartum women at KRRH, including BMI, obstetric history, chronic conditions, and lifestyle behaviors Variable Frequencies (N = 376) Percentages (%) Pre-pregnancy BMI Underweight 64 17.0 Normal 243 64.6 Overweight 48 12.8 Obese 21 5.6 Number of pregnancies 1–2 78 20.7 3–5 201 53.5 > 5 97 25.8 Number of live births 1–2 76 20.2 3–5 205 54.5 > 5 95 25.3 Diabetes during pregnancy Yes 101 26.9 No 275 73.1 Pre-eclampsia Yes 87 23.1 No 289 76.9 Current smoker Yes 12 3.2 No 364 96.8 Consumes alcohol Yes 127 33.8 No 249 66.2 Follow a special diet during pregnancy or postpartum Yes 104 27.7 No 272 72.3 Frequency of exercise 0.0 Never 129 34.3 Rarely 170 45.2 Regularly 77 20.5 Family history of cardiovascular disease Yes 91 24.2 No 285 75.8 Hypertension Normal 287 76.3 Abnormal 89 23.7 Mother diabetic No 310 82.4 Yes 66 17.6 Prevalence of Maternal Dyslipidemia More than half of the women in this study had an abnormal lipid profile, indicating maternal dyslipidemia. The incidence of maternal dyslipidemia was 58.2% (219 women out of 376, 95% confidence interval 53.2% to 63.2%). This is shown in Fig. 1 , which indicates that three out of five women presenting in the postpartum period have at least one type of dyslipidemia. The other 41.8% (157 women out of 376) were all normallipidemic, and this is an indication of the high prevalence of abnormalities in maternal lipids, even among those women without known antepartum metabolic diseases. Table 3 illustrates the spread of Lipid Abnormalities among the study participants. Low levels of high-density lipoprotein (HDL) were the most prevalent, affecting 71.5% of women. High levels of triglycerides and low-density lipoprotein (LDL) were also common, affecting 39.9% and 41.2% of women, respectively. High total cholesterol was the least prevalent, affecting 21.8% of women. Most women presented with two or more abnormalities, low HDL co-present with high TG or high LDL being very common. The confidence intervals showed high accuracy, ranging from 69.6% to 78.4% Table 3 Types of lipid abnormalities commonly observed Lipid Abnormality Frequencies (N = 376) Percentages (%) 95%CI HDL < 50mg/dL 269 71.5 69.6–78.4 ≥ 50mg/dL 107 28.5 21.6–30.4 Triglycerides < 150(mg/dL) 226 60.1 56.1–64.3 ≥ 150(mg/dL) 150 39.9 35.7–43.9 LDL < 130 mg/dL 221 58.8 56.5–60.4 ≥ 130 mg/dL 155 41.2 39.4–43.5 Total Cholesterol < 200 mg/dL 294 78.2 77.9–80.2 ≥ 200 mg/dL 82 21.8 19.8–22.1 Results on maternal lipids revealed a high prevalence of dyslipidemia, with low levels of the protective high-density lipoprotein, known as HDL, or “good” cholesterol. The average level of HDL in dyslipidemic women was 38.7 mg/dL, with a standard deviation of ± 6.1. The high prevalence of triglycerides and LDL-cholesterol, the types associated with increased risk, was shown by high average levels of 162.4 mg/dL and 128.7 mg/dL, with standard deviations of ± 54.3 and ± 36.5, respectively. The level of hypercholesterolemia (TC ≥ 200 mg/dL) was found to be 21% among women, with an average level of total cholesterol of 188.9 mg/dL and a standard deviation of ± 44.0. The presence of maternal dyslipidemia was significantly associated with various unfavorable neonatal outcomes (Table 4 ). There was an increased incidence of preterm birth in women with dyslipidemia (21.9%) as compared to those with normolipidemia (9.6%) (χ² = 9.15, p = 0.0025). Neonatal hypoglycemia was significantly associated, with 84.2% cases being from the dyslipidemic group (p = 0.018). The incidence of neonatal jaundice requiring treatment was significantly high in the infants of dyslipidemic women (44.3% as compared to 22.9% in normolipidemic women; χ² = 17.33, p < 0.001). However, NICU admissions, early neonatal deaths, and low 5-minute Apgar scores were similar in both groups. The incidence of macrosom In general, maternal dyslipidemia is strongly associated with prematurity, neonatal hypoglycemia, and jaundice, and this is likely due to the metabolic and placental effects of dyslipidemia. Table 4 Correlation between maternal Dyslipidemia and Adverse Neonatal Outcomes among Women Admitted in the Postnatal Unit at KRRH. Caption: Maternal dyslipidemia and neonatal outcomes are related. Preterm birth, neonatal hypoglycemia, and jaundice were all substantially correlated with dyslipidemia (p < 0.05). Neonatal Outcome Maternal dyslipidaemia Chi-square (χ²) p-value No 157(41.8) Yes 219(58.2) Preterm (n = 63) Yes 15 48 9.15 0.0025* No 142 171 Macrosomia (n = 52) 0.29 0.59 Yes 24 28 No 133 191 Hypoglycemia (n = 19) COR = 4.05 0.018* Yes 3 16 No 154 203 Jaundice (n = 133) 17.33 < 0.001 * Yes 36 97 No 121 122 NICU Admission (n = 126) 0.039 0.84 Yes 54 72 No 103 147 Early Neonatal death (38) 2.89 0.08 Yes 18 20 No 139 80 Low Apgar (67) 2.28 0.13 Yes 34 33 No 123 186 *Statistically significant, P < 0.05 Overall, approximately 35% of babies born to dyslipidemic mothers required specialized care (NICU admission or treatment for complications) compared to 28% born to normolipidemic mothers, although this composite difference was not statistically tested. Interestingly, although there were more premature births among dyslipidemic mothers, birth weights in term infants did not significantly differ-implying that maternal hyperlipidemia did not result in a higher risk of macrosomic infants in this population. This is in contrast to several reports that have associated maternal hypertriglyceridemia with fetal overgrowth[4], but in our setting other factors may have blunted that effect, such as the predominance of younger mothers or undernutrition in some women. Factors Associated with Maternal Dyslipidemia Bivariate analysis examined maternal sociodemographic, medical, obstetric, and lifestyle variables associated with dyslipidemia (Table 5 ). Several of these variables had strong unadjusted associations. There was a clear gradient in education: mothers who had no formal education had substantially higher dyslipidemia prevalence, 70.5%, compared to those with tertiary education, 43.7%, yielding a crude odds ratio of 3.08 (95% CI: 1.68–5.64, p < 0.001). Primary education also had higher odds, COR = 1.91, p = 0.02. These findings suggest that low educational attainment, perhaps due to reduced access to health information, low income, or limited health literacy, independently contributes to poor lipid profiles. Marital status was borderline significant, with married women showing a somewhat higher prevalence of dyslipidemia than single women: 61.1% versus 49.5%, respectively (COR = 1.60, p = 0.051). Higher household income (> 1,000,000 UGX per month) was surprisingly associated with greater odds of dyslipidemia compared to lower income (< 500,000 UGX), COR = 2.23 (p = 0.0058). The pattern may reflect dietary transitions associated with higher socioeconomic status, including increased consumption of processed and calorie-dense foods. Dyslipidemia was strongly associated with medical and obstetric factors. Prepregnancy obesity was a strong predictor: 85.7% of obese women were dyslipidemic, as compared with approximately half of normal-weight women (COR = 5.85, p = 0.0055). High gravidity and parity also increased risk. More than five pregnancies conferred COR = 2.46 (p = 0.0044), and more than five live births conferred COR = 3.29 (p = 0.0003), suggesting cumulative metabolic strain from successive pregnancies. Pregnancy complications showed striking associations with dyslipidemia. Gestational diabetes increased prevalence to 73.3% with COR = 2.45 (p = 0.001) and that of pre-eclampsia to 74.7% with COR = 2.59 (p = 0.015), consistent with well-known metabolic disturbances including insulin resistance, vascular dysfunction, and abnormalities of lipid metabolism. In fact, some of the most potent associations were captured for lifestyle factors. Though not as common, cigarette smoking was associated with a higher prevalence of dyslipidemia, 75% versus 57.7% (COR = 2.20, p = 0.038). Alcohol use was also associated with higher prevalence of dyslipidemia, 71.7% versus 51.4% (COR = 2.39, p = 0.017). Physical inactivity emerged as a major risk factor, as 70.5% of women who never exercised were found to be dyslipidemic (COR = 5.98, p < 0.001), with a dose-response trend across those exercising rarely, 62.4% (COR = 4.15, p = 0.012). Neither family history of cardiovascular disease nor baseline hypertension demonstrated strong crude associations, though elevated blood pressure fulfilled criteria for inclusion in multivariable analysis. Overall, the bivariate analysis highlighted many variables, especially obesity, high parity, gestational diabetes, pre-eclampsia, smoking, alcohol use, and low physical activity, to be significantly associated with maternal dyslipidemia. These were then carried forward into multivariable logistic regression to control for confounding. Table 5 Bivariate regression analysis of the factors associated with maternal Dyslipidemia. Variables Maternal Dyslipidemia COR(95%CI) P-value No 157(41.8) Yes 219(58.2) Age (years) < 20 44(46.8) 50(53.2) Ref 20–29 40(42.1) 55(57.9) 1.21(0.6812–2.149) 0.5155 30–39 41(38.7) 65(61.3) 1.39(0.795–2.4497) 0.2463 40+ 32(39.5) 49(60.5) 1.35(0.7379-2.460) 0.3316* Marital status Single 46(50.5) 45(49.5) Ref Married 111(38.9) 174(61.1) 1.60(0.996–2.576) 0.051 Education level No education 26(29.5) 62(70.5) 3.08(1.67-5.9265) < 0.0008* Primary 44(40.4) 65(59.6) 1.906(1.0406-3.49) 0.0367* Secondary 47(43.5) 61(56.5) 1.67(0.9155–3.063) 0.0942 Tertiary 40(56.3) 31(43.7) Ref Occupation Unemployed 72(45.9) 85(54.1) Ref Self-employed 54(38.6) 86(61.4) 1.35(0.849–2.14) 0.205 Formal employment 31(39.2) 48(60.8) 1.31(0.756–2.2733) 0.3338 Monthly household income 1,000,000/- 23(28.4) 58(71.6) 2.23(1.262–3.956) 0.0058 Pre-pregnancy BMI Underweight 32(50.0) 32(50.0) 0.975(0.562–1.692) 0.930 Normal 120(49.4) 123(50.6) Ref Overweight 20(41.7) 28(58.3) 1.37(0.730–2.555) 0.3294 Obese 3(14.3) 18(85.7) 5.85(1.680-20.388) 5 29(29.9) 68(70.1) 2.46(1.326–4.5946) 0.0044* Number of live births 1–2 40(52.6) 36(47.4) Ref 3–5 93(45.4) 112(54.6) 1.33(0.789–2.267) 0.279 > 5 24(25.3) 71(74.7) 3.29(1.723–6.269) 0.0003* Diabetes during pregnancy Yes 27(26.7) 74(73.3) Ref No 130(47.3) 145(52.7) 2.45(1.38–4.13) 0.001* Pre-eclampsia Yes 22(25.3) 65(74.7) 2.59(1.50–4.47) 0.015* No 135(46.7) 154(53.3) Ref Current smoker Yes 3(25.0) 9(75.0) 2.20(1.60–6.01) 0.038* No 154(42.3) 210(57.7) Ref Consumes alcohol Yes 36(28.3) 91(71.7) 2.39(1.54–3.70) 0.017* No 121(48.6) 128(51.4) Ref Follow a special diet during pregnancy or postpartum Yes 40(38.5) 64(61.5) Ref No 117(43.0) 155(57.0) 1.21(0.90–1.63) 0.17* Frequency of exercise Never 38(29.5) 91(70.5) 5.98(3.19–11.22) < 0.001* Rarely 64(37.6) 106(62.4) 4.15(2.35–7.33) 0.012* Regularly 55(71.4) 22(28.6) Ref Family history of cardiovascular disease Yes 31(34.1) 60(65.9) 1.60(0.76–2.71) 0.62 No 129(45.3) 156(54.7) Ref Hypertension No 132(46.0) 155(54.0) Ref Yes 24(27.0) 65(73.0) 2.31(1.39–3.83) 0.03* Mother diabetic No 134(43.2) 176(56.8) Ref Yes 22(33.3) 44(66.7) 1.53(0.88–2.69) 0.13* *Statistically significant, p < 0.2 In all, the bivariate results suggested that education, income, obesity, high gravidity, gestational diabetes, pre-eclampsia, smoking, alcohol, and physical inactivity all showed significant or borderline-significant associations with maternal dyslipidemia. Each of these factors was then included in the multivariable model in order to determine those factors acting as independent predictors of maternal dyslipidemia when adjusted for all others. Multivariable logistic regression showed that after adjusting for potential confounding factors simultaneously, four factors emerged as independent predictors of maternal dyslipidemia in this population. Adjusted Odd Ratios are presented in Table 6 and are considered significant if p < 0.05. Pre-pregnancy obesity remained a strong predictor in that obese women were found to be more than twice as likely to develop dyslipidemia as women of normal weight (AOR = 2.17, 95% CI 1.73–6.31, p = 0.04). This agrees with the fact that obesity is often associated with insulin resistance and a pro-atherogenic lipid profile[20]. After adjustment, overweight (AOR 1.25) and underweight (AOR 0.56) were not significant; this reflects a threshold effect since only obesity (BMI ≥ 30) was associated with significantly higher risk. Pre-eclampsia in the index pregnancy was independently associated with maternal dyslipidemia: AOR = 2.07; 95% CI 1.67–6.43, p = 0.02. This indicates that women with hypertensive disorders of pregnancy were roughly twice as likely to have abnormal lipids, adjusting for BMI and age, among other factors. The pathophysiology of pre-eclampsia includes endothelial dysfunction and oxidative stress, events that might be exacerbated or lead to dyslipidemia. Smoking remained an important risk factor, with a fully adjusted odds ratio for dyslipidemia of 2.36 (95% CI 1.93–4.62, p = 0.01) for current smokers versus non-smokers. Thus, even in this minimally smoking population, tobacco use remained a powerful risk factor for abnormal lipids. The effect of smoking is probably explained by the facts that smoking reduces HDL cholesterol and increases LDL oxidation. Physical inactivity remained an independent predictor. In particular, mothers who never exercised were almost three times more likely to have dyslipidemia than those who exercised regularly (AOR = 2.74, 95% CI 1.63–7.48, p = 0.04). Those who exercised rarely still had higher odds (AOR 1.67) but this was not significant after adjustment (p = 0.25), suggesting that the most sedentary behavior-no exercise at all-is the critical risk level. Regular physical activity is likely to confer protective effects on lipid metabolism, as reflected by much lower dyslipidemia rates in that group. Several other factors were entered into the model but did not remain independently associated when controlling for other variables. For example, low education (no schooling) had an AOR of 1.94 (95% CI 0.86–4.15, p = 0.07) compared to tertiary education – a sizeable increase but not statistically significant in the adjusted model. Household income > 1,000,000 UGX was no longer significant (AOR ~ 1.05, p = 0.32) when other confounders were considered, perhaps due to its effect being mediated through other lifestyle variables that are associated with income, such as diet. Similarly, gestational diabetes, significant in univariate analysis, did not retain significance in the final model (AOR 1.84, 95% CI 0.93–5.72, p = 0.09), which may have been because many women with GDM also had high BMI or developed pre-eclampsia, which were accounted for. Similarly, parity and alcohol use did not reach significance after adjustment. Of note, the variable for "maternal history of diabetes" did not reach statistical significance (AOR 1.49, p = 0.36) - while genetic factors are very important, they may play a smaller observable role in the presence of very strong lifestyle and pregnancy-related factors. Table 6 Multivariate regression analysis of the factors associated with maternal Dyslipidemia among Women Admitted in the Postnatal at Unit KRRH. Caption: Maternal dyslipidemia's independent predictors were found using multivariate logistic regression. After adjustment, smoking, obesity, pre-eclampsia, and physical inactivity were still significant. Variables COR(95%CI) P-value AOR(95% CI) P-value Age (years) < 20 Ref 20–29 1.21(0.72–2.03) 0.47 1.15(0.49–5.94) 0.56 30–39 1.39(0.83–2.33) 0.21 1.24(0.37–4.14) 0.41 40+ 1.35(1.08–2.82) 0.09 1.25(0.91–6.31) 0.13 Education level No education 3.08(1.68–5.64) < 0.001 1.94(0.86–4.15) 0.07 Primary 1.91(1.08–3.38) 0.02 1.29(0.54–3.62) 0.15 Secondary 1.67(0.45–2.97) 0.54 1.03(0.73–5.95) 0.67 Tertiary Ref Pre-pregnancy BMI Underweight 0.98(0.58–1.66) 0.94 0.56(0.15–5.67) 0.44 Normal Ref Overweight 1.37(0.74–2.55) 0.31 1.25(0.53–4.18) 0.25 Obese 5.85(1.63–7.62) 5 2.46(0.87–4.74) 0.19 1.91(0.71–6.92) 0.14 Number of live births 1–2 Ref 3–5 1.33(0.81–2.20) 0.26 1.27(0.35–4.15) 0.46 > 5 3.29(0.87–6.65) 0.07 1.93(0.61–7.04) 0.27 Diabetes during pregnancy Yes No 2.45(1.38–4.13) 0.001 1.84(0.93–5.72) 0.09 Pre-eclampsia Yes 2.59(1.50–4.47) 0.015 2.07(1.67–6.43) 0.02* No Ref Current smoker Yes 3.10(1.60–6.01) 0.038 2.36(1.93–4.62) 0.01* No Ref Consumes alcohol Yes 2.39(1.54–3.70) 0.017 1.54(0.87–2.19) 0.15 No Ref Follow a special diet during pregnancy or postpartum Yes Ref No 1.21(0.90–1.63) 0.17 1.05(0.67–2.18) 0.32 Frequency of exercise Never 5.98(3.19–11.22) < 0.001 2.74(1.63–7.48) 0.04* Rarely 4.15(2.35–7.33) 0.012 1.67(0.78–4.32) 0.25 Regularly Ref Hypertension No Ref Yes 2.31(1.39–3.83) 0.03 1.76(0.54–3.17) 0.32 Mother diabetic No Ref Yes 1.53(0.88–2.69) 0.13 1.49(0.73–2.73) 0.36 *statistically significant, P < 0.05 After adjustment, the following four variables emerged as being particularly important, and all are related to 2- to 3-fold increases in the odds of dyslipidemia: maternal obesity, pre-eclampsia, smoking, and lack of exercise. These are likely the main target for intervention. The relationships represented by education and gestational diabetes, as evident from crude models, are accounted for by and fail to reach significance in the presence of other variables. Discussion In this cross-sectional study involving 376 women in the postpartum period in a regional hospital in Uganda, we identified a high prevalence of maternal dyslipidemia and significant associations with certain neonatal outcomes and maternal risk factors. It appears that this is one of the first studies to have assessed maternal lipid profiles and pregnancy outcomes in this manner in Uganda, and implications for practice are significant, as they pertain to low resource settings. Prevalence of dyslipidemia We found that 58.2% of the mothers aged under 30 were dyslipidemic (at least one value was abnormal). This value is much higher than the previous global estimates of dyslipidemia during pregnancy, ranging from 15% to 40%( 15 , 16 ). Our value does, however, appear consistent with the more recent literature from low and middle-income countries. Thus in Ghana found over half the lactating women to have hypercholesterolemia or low HDL. Likewise( 17 ), in China, found the prevalence of dyslipidemia to reach 53.7% within one month of delivery, and increase with raised BMI( 18 ). The close similarity between our findings and those of other studies indicates that dyslipidemia is becoming increasingly common within women of childbearing age in all parts of the globe, likely in response to global lifestyle and diet trends. Also, note the remarkably high proportion (.72% of our group) of low HDL-C. Low HDL is commonly associated with high prevalence rates of MetS, VA, and high-carb diet. Notwithstanding the low intake of animal fats, the diet in rural Uganda is high in carbohydrates and low in dietary fats, which may underlie low levels of HDL-cholesterols. On the other hand, the physiological hyperlipidemia of late pregnancy may as yet not be entirely resolved by the immediate puerperium, thereby artificially inflating the percentages of abnormalities( 19 ). Also, pregnancy-related abnormalities, including volume and hormonal effects, may play significant parts in artificially altering levels of lipids immediately following delivery. Our study’s lipid profile, dominance by low levels of HDL and high levels of LDL/TG, reflects a status reminiscent of those within the South Asian and African populations, as opposed to those within the Western populations. In point of fact, within Ethiopia, it was shown that both TG and cholesterol levels were increasingly high in pregnancy, and indeed, many women were found to be above the normal cut-off thresholds by the third trimester( 12 ). The large percentage of women in our Ugandan study with dyslipidemia confirms the point that we are dealing with both issues of undernutrition and those pertaining to overnutrition within our maternal population. Neonatal Outcomes In this analysis, we identified that maternal dyslipidemia significantly increased the incidence of preterm birth. The prevalence of preterm birth in dyslipidemic women is 21.9%, which is well over twice the incidence in women with normal lipids (9.6%). This significant association persisted after controlling for the confounding variables, suggesting that dyslipidemia, or factors associated with it, play some part in mechanisms of preterm labor and medically indicated early delivery. The mechanism may be hypothesized as follows that is, the ‘atherosclerosis’ of the uteroplacental vascular bed, secondary to high levels of LDL and triglicerides, may establish placental insufficiency, causing preterm delivery( 20 ). Another mechanism may propose dyslipidemia as ‘marker’ for other undiagnosed cases of maternal metabolic syndromes and undiagnosed cases of Diabetes, both well recognized to increase rates of preterm deliveries due to spontaneous term and induced preterm deliveries (in preeclampsia, for example). Our observation is supported other studies, wherein Jiang et al. carried out this meta-analysis and reported in 2017, as follows—that is, ‘maternal hypertriglyceridemia during pregnancy significantly increased the risk of preterm birth, with an odds ratio of overall 1.5 to 2'( 21 ). In similar observation conducted on Nigeria by ‘Ottun et al. multicentric cohort studies, 2022,’ they have now been able to show as under—that is, ‘Hyperlipidemia is significantly associated with increased incidence of spontaneous preterm delivery'( 21 ). Our observations, thereby, add to observations obtained from East-African studies on this subject, indicating the significant correlation and doubly important contribution of dyslipidemia as ‘risk factors’ for ‘prematurity,’ and hence, important implications within the context of reducing neonatal mortality presenting as ‘preterm birth.’ Notably, we did not establish any significant relationship between maternal dyslipidemia and macrosomic birth within our study population. This may seem contrary to other studies, which have shown that high maternal cholesterol and triglycerides are both risk factors for macrosomic birth because of the resultant overgrowth and high birthweight of the baby( 22 ). For instance conducting a study within Iraq, found high TG and LDL within late gestation and high chances of giving birth to macrosomic babies( 23 ). The incidence of macrosomic birth in our study did not show any variation based on the status of the mother’s dyslipidemia. The reason here may be the overshadowing effect of controlling blood sugar on macrosomic birth, as opposed to other factors like dyslipidemia. Many of the women within our study were either obese or suggested cases of metabolic syndromes, yet they were all not giving birth to large babies. Possibly, this may be an indication of other factors within this population, including low intake and genetic issues, hindering growth within fetuses, as evidenced by low birthweight within dyslipidemic women, likely linked to the high proportion of preterm deliveries within this group. Yet, an important consideration here may be linked to the point in the woman’s life where lipids were measured, as it was carried out after birth, and the main growth within the fetus may be linked to measurements and determination within late-third gestations. Some women may likely develop high blood lipids following child birth, likely linked to the point of initiation within lactations and high fat reserve mobilization within this same lactating phase, and this will, therefore, play no significant part in influencing growth within fetuses. Our conclusion, therefore, may likely show otherwise, within this Ugandan setting, than within other populations, as they seemingly show significant predictable relationships within dyslipidemia and macrosomic birth within other environments. We did not observe any significant effect of dyslipidemia on Apgar score, NICU admission, and early neonatal deaths after adjusting for the presence of prematurity. This indicates that, except for the mentioned metabolic issues, the overall newborn health status, as affected by dyslipidemia, may have less significance at birth or is possibly confounded by gestational age. The early neonatal deaths among our cohort were largely restricted to either extreme preterm and infectious cases, in whom the mother’s lipid level is probably not crucial. Maternal risk factors for dyslipidemia : Our multivariable analysis identified four independent risk factors: obesity, pre-eclampsia, smoking, and physical inactivity. All of these are well-aligned with existing knowledge, lending credibility to our findings. Pre-pregnancy obesity emerged as one of the strongest predictors, which is expected since obesity is characterized by insulin resistance and an atherogenic lipid profile (high TG, high LDL, low HDL). This is in line with results from other studies; for instance demonstrated that higher BMI in lactating women was associated with significantly greater odds of dyslipidemia( 18 ). Similarly, a study in Ethiopia's general population found obesity to correlate strongly with dyslipidemia prevalence( 14 ). In our context, with 41% of women overweight or obese, the rising trend of maternal obesity foretells a concurrent rise in dyslipidemia. Public health measures to reduce obesity in women of reproductive age could thus have the added benefit of improving lipid profiles and possibly pregnancy outcomes. Of particular note is the association of pre-eclampsia with dyslipidemia. Pre-eclampsia has been referred to as a “cardiovascular accident” of pregnancy and shares risk factors with CVD, including dyslipidemia. We found a two-fold higher odds of dyslipidemia in women with pre-eclampsia. This is in line with the notion that endothelial dysfunction and oxidative stress in pre-eclampsia may be exacerbated by high levels of LDL and triglycerides( 12 ). In fact, hypertriglyceridemia is thought to play a role in the pathogenesis of pre-eclampsia by contributing to small dense LDL formation and endothelial damage. study reported that pregnant women with hyperlipidemia had increased risk for developing pre-eclampsia( 24 ). Our findings support this bidirectional link: pre-eclamptic women should perhaps be evaluated for metabolic risk factors, and conversely, women with significant dyslipidemia might warrant closer blood pressure surveillance in pregnancy. Many lifestyle factors were clearly implicated. Smoking doubled the odds of maternal dyslipidemia. The negative impact of smoking on lipid profiles was well known in non-pregnant populations, particularly through reducing HDL-C and raising LDL oxidation( 25 ). Our study indicates this holds true for pregnant and postpartum women as well. Clinical implications point to the reinforcement of smoking cessation programs targeting pregnant women. Decreasing smoking may improve not only pregnancy outcomes but also maternal lipid and cardiovascular health. Another independent predictor was physical inactivity. Women who did not exercise at all had approximately 2.7 times higher odds for dyslipidemia. Physical activity is known to enhance HDL levels and improve overall lipid metabolism. Even moderate exercise during pregnancy can blunt the increase in triglycerides and enhance insulin sensitivity. That inactivity remained significant even after adjusting for obesity suggests that exercise has benefits beyond weight control-perhaps through an enzymatic improvement in lipid profiles, including upregulation of lipoprotein lipase. Our data thus support studies among diverse populations showing a correlation of sedentary behavior with dyslipidemia( 26 ). This suggests that encouraging regular physical activity during pregnancy, tailored to maternal tolerance, may be an important intervention in preventing dyslipidemia. Culturally appropriate exercises and antenatal exercise classes could thus be useful in our setting. Some factors were not retained in the multivariate model but are worth mentioning. Low education and low income were related to higher dyslipidemia in a crude analysis presumably due to poor health literacy and fewer means to support a healthy diet or lifestyle. In the Ghana study, low education was one of the predictors of low HDL status in women( 17 ). While education itself fell out of our final model, lifestyle variables are often intertwined with education; therefore, health education focused on less-educated mothers could have indirect benefits on dyslipidemia. We also found gestational diabetes to be associated with dyslipidemia in bivariate analysis, reflecting other studies; for example, study found GDM mothers had 5.6-fold higher odds of dyslipidemia( 22 ). The impact of GDM appears mediated by obesity and other variables in our cohort, as it lost its significance in the full model. However, the high prevalence of GDM suggests that this particular group of individuals may serve as a metabolic subgroup at high risk. Clinicians should consider checking lipid profiles in women diagnosed with GDM as part of a comprehensive assessment of cardiovascular risk( 27 ). Strengths and limitations: This study has identified an under-investigated aspect in a low-resource African setting and hence contributes to local evidence on a global problem. We had a fairly large sample size and data on a wide range of variables were collected in a systematic way, enabling multivariable adjustment to isolate independent effects. The use of hospital laboratory assays for lipid profiling adds reliability to the biochemical data, and we followed standardized definitions for outcomes and exposures, enhancing the reproducibility of our work. However, the study also has important limitations. First, its cross-sectional design limits causal inferences. We measured postpartum lipid levels, after the occurrence of neonatal outcomes, which complicates any direct cause-effect interpretation. While it is likely that maternal lipid status in late pregnancy was correlated with what we measured postpartum, acute peripartum changes may influence lipid levels. For instance, the stress of labor or fasting during labor may transiently alter the value of lipids. Serial measurements during the course of pregnancy, if possible, would provide a clearer picture of the timing and impact of dyslipidemia. Secondly, given that this is a hospital-based study, we must note the possibility that our findings may not be generalizable to all pregnant women in the community, particularly those who do not deliver in health facilities. At a minimum, the women in our study delivered at a regional referral hospital, which could indicate some level of access to care; women delivering at home or at smaller centers might differ in their risk profile. Thirdly, we did not obtain detailed dietary intake data-e.g., fat, sugar, or micronutrient consumption-which would be an important confounder of lipid levels. There is no doubt that diet during pregnancy and the puerperium affects lipid metabolism, and future studies should include a nutritional assessment. Fourth, we relied on single measurements of lipids and glucose (we did not systematically perform oral glucose tolerance tests to identify all GDM cases); hence, some misclassification is possible-e.g., some dyslipidemic mothers may have had undiagnosed hyperglycemia. Fifth, we did not do advanced lipid testing-like Apo lipoproteins or particle size analysis-which might better characterize risk; we kept to conventional lipid panels available in our setting. Implications Despite limitations, our study highlights a few actionable points. The high rate of dyslipidemia among postpartum women indicates the need to consider routine lipid screening as part of antenatal or postnatal care, especially in the presence of risk factors. Presently, antenatal guidelines in Uganda do not include lipid testing, focusing more on infections, anemia, and hypertensive disorders. Given the findings, integrating a simple lipid profile (that has become more affordable) could help identify at-risk women who might benefit from dietary counseling or closer monitoring. In addition, the associations with smoking and inactivity suggest that existing antenatal education should be extended to also cover lifestyle counseling for nutrition, exercise, and smoking cessation. Such interventions are inexpensive and may improve lipid levels and overall pregnancy health. For example, moderate exercise could be recommended for pregnant women (when there are no contraindications) and could be included in antenatal classes. Nutritional counseling should highlight balanced diets with healthy fats (sources of omega-3) and fiber that might improve HDL and lower LDL. It also suggests that the link between dyslipidemia and preterm birth and neonatal complications like hypoglycemia means that women identified with high lipid levels may need a higher intensity of surveillance during pregnancy, including potentially earlier or more frequent ultrasound monitoring for fetal growth and well-being, and preparation for possible preterm delivery, such as offering antenatal corticosteroids if risks become apparent. Neonatal caregivers should be informed about all metabolic risk factors in a newborn's mother so they can proactively manage and monitor issues related to blood sugar and bilirubin. Our findings are also in agreement with the concept that pregnancy can act as a “stress test” for future maternal health. Pregnancy dyslipidemia, particularly when occurring in conjunction with other complications such as pre-eclampsia or GDM, is an independent risk factor for cardiovascular disease in later life. This should be an opportunity for follow-up after delivery: such mothers should be counseled and possibly enrolled into non-communicable disease prevention programs postpartum. Such interventions include weight management, healthy diet, physical activity, and smoking cessation; these offer dual benefits to prevent not only future cardiovascular disease in the mother but also improve outcomes of any subsequent pregnancies. Conclusion More than half of the postpartum women in our study had abnormal lipid profiles, highlighting that maternal dyslipidemia is a serious health concern even in the setting of a regional Ugandan hospital. Low HDL cholesterol was extremely common, and a sizable fraction of women also had high levels of LDL, triglycerides, or total cholesterol. Maternal dyslipidemia was associated with increased risks of preterm birth and neonatal complications such as hypoglycemia and jaundice, complications with the potential to have serious short- and long-term sequelae for the child. We identified pre-pregnancy obesity, pregnancy-related hypertensive disorders, cigarette smoking, and physical inactivity as key modifiable factors associated with dyslipidemia in these women. These findings emphasize the urgent need for greater attention to be paid to cardiovascular and metabolic health in antenatal and postnatal care. Routine lipid screening in pregnancy (or early postpartum) could facilitate the early identification of women who are at risk. Coupled with this, lifestyle modification interventions, including dietary improvements, promotion of exercise, and smoking cessation programs for pregnant women, are recommended in an attempt to mitigate dyslipidemia. The importance of addressing maternal dyslipidemia goes beyond improving pregnancy and neonatal outcomes in terms of reduced preterm births and neonatal morbidities but may also provide long-term health benefits by decreasing the mother's future cardiovascular risk. Therefore, maternal dyslipidemia needs to be identified, alongside anemia and infections, as an important component of maternal health that requires surveillance and intervention in resource-poor settings. Recommendations Based on our study findings, we propose the following recommendations for the improvement of maternal and neonatal outcomes related to dyslipidemia. Include Lipid Screening during Antenatal Care : Health facilities may now consider adding a basic lipid profile test to the care for pregnant women, particularly in mid or late pregnancy, and more especially so in those with risk factors including obesity, advanced maternal age, or history of pre-eclampsia/diabetes. Early detection of dyslipidemia would thus enable timely nutritional and medical interventions. Targeted Nutritional Guidance : Enhance antenatal and postnatal nutritional education to focus on healthy diets that will improve lipid profiles. This includes increasing fruit, vegetable consumption, and sources of healthy fats, such as fish rich in omega-3, while decreasing intake of processed carbohydrates, sugary drinks, and transfats. Culturally appropriate diet plans need to be developed and, where possible, include family members so they can support the mother's dietary changes. Promote Physical Activity Programs : Implement community- and facility-based physical activity programs to ensure safe physical activity among women before, during, and after pregnancy. Brief exercises or demonstrations can be given in antenatal clinics. Community health workers need to be trained to counsel pregnant women on active lifestyles as a prevention strategy for excessive weight gain and dyslipidemia. Smoking Cessation Support : Screening for tobacco use should be conducted on all pregnant women, with referral to counseling and support for quitting, including behavioral support and connection with cessation programs. Given the strong association between smoking and dyslipidemia, as well as other complications associated with pregnancy, it is of paramount public health policy importance to encourage smoking cessation in pregnancy. Management of identified dyslipidemia : In women with significantly abnormal lipid levels during pregnancy, closer monitoring and follow-up may be contemplated. While this is not a time for the institution of lipid-lowering medication (such as statins, which are contraindicated), such women should be followed in a high-risk clinic setting. Postpartum follow-up should be arranged to reassess lipid levels and start medical therapy if indicated after breastfeeding, to protect long-term health. Enhanced Care for Vulnerable Neonates : Neonatal teams need to be prepared for babies born to mothers with dyslipidemia or related metabolic conditions. These infants would require earlier blood glucose screening for hypoglycemia and more timely bilirubin screening. Maternity services could have protocols such that maternal metabolic flags can automatically trigger neonatal precautionary measures, such as feeding support to avoid hypoglycemia and early jaundice assessment. Community Awareness and Lifestyle Programs : At the community level, create awareness that obesity, poor nutrition, and cigarette smoking have implications on pregnancy outcomes as well as being causes of chronic diseases later in life. Community health forums should address the prevention of "lifestyle diseases" in young women. The participation of local leaders, coupled with the use of mass media, may alter perceptions to accept active exercise and proper nutrition as crucial for female health. Further Research : Prospective studies on the trend of changes in lipids during pregnancy and their direct causality with outcomes. Further research will also be warranted on cost-effective interventions (like nutritional supplements or specific diets in pregnancy) that may help improve lipid profiles in low-resource settings. Indeed, exploring the genetics that underpin the dyslipidemia in African women could provide another key in the future to personalized approaches. The recommendations will be multidisciplinary in nature, led by obstetricians along with midwives, nutritionists, public health practitioners, and policy-makers. Working toward healthier mothers and babies through the prevention, early detection, and management of maternal dyslipidemia will help reduce not only neonatal complications but also cardiovascular disease in the future. Abbreviations 1. TC Total Cholesterol 2. LDL C –Low–Density Lipoprotein Cholesterol 3. HDL C –High–Density Lipoprotein Cholesterol 4. TG Triglycerides 5. GDM Gestational Diabetes Mellitus 6. KRRH Kayunga Regional Referral Hospital 7. NICU Neonatal Intensive Care Unit Declarations Human Ethics and Consent to Participate This study received ethical approval from the Kampala International University Research Ethics Committee (KIU-REC-2025-862) and administrative clearance from Kayunga Regional Referral Hospital (KRRH) and was registered with the Uganda National Council for Science and Technology (UNCST). All participants were ≥ 18 years and provided written informed consent at the postnatal care visit after a clear explanation of study procedures, ensuring informed and voluntary participation. The study adhered to the ethical principles of the Declaration of Helsinki. Consent for Publication Not applicable. This manuscript does not contain any individual person's data in any form. Competing Interests The authors declare no competing interest. Clinical Trial Registration Not applicable. Funding The authors declare that no funds, grants, or other support were received during the study. Author Contribution Fathi designed and developed the proposal. Sawdo and fathi performed the data collection and entry. MF.Ismail performed the statistical analysis. MF.Ismail drafted the initial manuscript. Tayrab, Rukamba, Intisar and joseph contributed to reviewing and revising the manuscript. The final manuscript was read and approved by all the authors. Acknowledgements We extend our gratitude to the research assistants and participants. Data Availability The dataset that was utilized in this study is not publicly available due to ethical considerations. Upon reasonable request, the dataset used can be accessed with the permission of the corresponding author Dr. Fathi Abdi Farah (email: [ [email protected] ](mailto: [email protected] ) ) References Sun L, Gao B, Wang M, Liu Y, Shan Z, Teng W et al. The establishment of lipid profiles reference ranges during pregnancy: a systematic review and meta-analysis. Reprod Biol Endocrinol. 2025 July 28;23(1):110. Guo J, Qiu H, Wang J, Liu X, Chen S, Li B. Correlation between lipid metabolism levels and pregnancy outcomes. Front Med. 2025;12:1530525. Formisano E, Proietti E, Perrone G, Demarco V, Galoppi P, Stefanutti C et al. Characteristics, Physiopathology and Management of Dyslipidemias in Pregnancy: A Narrative Review. Nutrients. 2024 Sept 1;16(17):2927. 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Farah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3PMQrCMBSA4ZRAsgS7tqh4hUpBJ9urGDK4dHMSB5VCJw/j9MRNEHR0LXQxFtwdBN1MKggubd0E8w9JCPlIgpDJ9IPROSJoqBYe4ouTmlmjirAtfpPY0xukFkEFGRJHL6oJPe5PMgo6fSqSyS0KWgRheU7LCBPU4yC6m6VMsjYI9TDi+1EJCZEgDoettUp5krmAFWGkWUaYnRck1GTswqwGcV63cE2sK+zqkLyniBDqL3HTggMjuOIvzOYX9wHBYE1H8vqAaWjTWOZl5CPMirHucZ11/+a0yWQy/U1Pr1BGS7qcqHQAAAAASUVORK5CYII=","orcid":"","institution":"Kampala International University","correspondingAuthor":true,"prefix":"","firstName":"Fathi","middleName":"Abdi","lastName":"Farah","suffix":""},{"id":565300953,"identity":"62ca54d4-09ae-41f4-92f4-7a346d53668f","order_by":1,"name":"Sowda Abdikarim Sheikh Isse","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Sowda","middleName":"Abdikarim Sheikh","lastName":"Isse","suffix":""},{"id":565300955,"identity":"d9327366-7837-4401-abc6-0d17b6fafda0","order_by":2,"name":"Okurut Emmanuel","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Okurut","middleName":"","lastName":"Emmanuel","suffix":""},{"id":565300957,"identity":"26f02649-2653-481d-a02d-026991057b83","order_by":3,"name":"Sadia Mahamuud Mahamed","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Sadia","middleName":"Mahamuud","lastName":"Mahamed","suffix":""},{"id":565300958,"identity":"f388d510-c292-4411-85eb-f8882d9662a9","order_by":4,"name":"Hafsa Abdullahi","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Hafsa","middleName":"","lastName":"Abdullahi","suffix":""},{"id":565300961,"identity":"f2719c42-ce1c-41ee-bdbb-0c2e61a526b4","order_by":5,"name":"Ramla Abdi Ali","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Ramla","middleName":"Abdi","lastName":"Ali","suffix":""},{"id":565300963,"identity":"da268d51-5d8c-4f52-983e-2626ffb54098","order_by":6,"name":"Okello Peter","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Okello","middleName":"","lastName":"Peter","suffix":""},{"id":565300964,"identity":"2a217373-0c0f-4216-943e-de19fc8db777","order_by":7,"name":"Eltayeb Mahamed Ahmed Tayrab","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Eltayeb","middleName":"Mahamed Ahmed","lastName":"Tayrab","suffix":""},{"id":565300965,"identity":"885f34a8-e335-4f53-bab6-840246e83849","order_by":8,"name":"Rukamba Jean Dieu","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Rukamba","middleName":"Jean","lastName":"Dieu","suffix":""},{"id":565300967,"identity":"531202b3-dbef-4434-9dbc-7551ed2312cd","order_by":9,"name":"Abdulkadir hassan Anod","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Abdulkadir","middleName":"hassan","lastName":"Anod","suffix":""},{"id":565300969,"identity":"b6061db8-820b-4f55-988a-3cb78b6c0d9f","order_by":10,"name":"Bahja Ahmed Mumin","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Bahja","middleName":"Ahmed","lastName":"Mumin","suffix":""},{"id":565300970,"identity":"cbd895af-de95-44cd-aecd-8d11280a8803","order_by":11,"name":"Abdulkarim Ismail Qarbote","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Abdulkarim","middleName":"Ismail","lastName":"Qarbote","suffix":""},{"id":565300971,"identity":"dfb6a4d6-a54b-441c-abff-08115eba41d0","order_by":12,"name":"Intisar Khalafalla","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Intisar","middleName":"","lastName":"Khalafalla","suffix":""},{"id":565300972,"identity":"61ad810b-f638-421c-a580-365d80b29d72","order_by":13,"name":"Mohamed Farah Ismail","email":"","orcid":"","institution":"Kampala International University","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Farah","lastName":"Ismail","suffix":""}],"badges":[],"createdAt":"2025-11-19 15:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8156962/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8156962/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99218407,"identity":"c0662471-d7e6-498b-92a5-87860968d528","added_by":"auto","created_at":"2025-12-30 09:19:14","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142507,"visible":true,"origin":"","legend":"","description":"","filename":"Munuscriptfathisecond.docx","url":"https://assets-eu.researchsquare.com/files/rs-8156962/v1/f3e41f12e184535857151138.docx"},{"id":99218405,"identity":"5d6ae011-ad2a-42e0-b13e-675cc3b414aa","added_by":"auto","created_at":"2025-12-30 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09:19:14","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179161,"visible":true,"origin":"","legend":"","description":"","filename":"812f50a00a084369bd3f0f762cae09b01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8156962/v1/e1fa2392585c71738f4c3470.xml"},{"id":99218411,"identity":"24163660-47fe-4c6c-ac70-7a2a69ba5f4d","added_by":"auto","created_at":"2025-12-30 09:19:14","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":198868,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8156962/v1/15b01b1298444bd7de3c0a34.html"},{"id":99317978,"identity":"54b8a987-a252-47d7-b6dc-d79461d9775f","added_by":"auto","created_at":"2025-12-31 16:31:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27814,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePrevalence of maternal dyslipidemia among postpartum women at Kayunga RRH (N=376). Over half (58.2%) of the mothers exhibited dyslipidemia (defined as any abnormal lipid value), while 41.8% had normal lipid profiles.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8156962/v1/28eff6efddd05773877a078d.png"},{"id":99323712,"identity":"f182528f-6cb2-4e73-b2e7-4ffc7d96d263","added_by":"auto","created_at":"2025-12-31 16:46:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2405019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8156962/v1/415412bc-adfd-4c57-ab82-0338f5990f93.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrevalence, Adverse Neonatal Outcomes, and Factors Associated With Maternal Dyslipidemia Among Women Admitted in the Postnatal Unit at Kayunga Regional Referral Hospital, Uganda\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePregnancy is associated with considerable changes within the mother\u0026rsquo;s lipid profiles. Physiologic pregnancy increases levels of cholesterol, LDL, and triglycerides, peaking in late pregnancy(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Yet, maternal dyslipidemia is defined as pathological abnormalities in lipid levels (elevated levels of TC, LDL-C, and TG, and low levels of HDL-C) above the norm during pregnancy(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The role of dyslipidemia in pregnancy is being increasingly appreciated, as this becomes increasingly relevant as a pregnancy complication affecting both mother and baby. Previous studies imply associations with hyperlipidemia and pregnancy-related complications in the mother, including those of pre-eclampsia and gestational diabetes mellitus, as well as those affecting neonates, including those of preterm and macrosomic babies(\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This, as evidenced by the meta-analysis conducted by Jiang et al. in 2017, which suggested significant relations of maternal pregnancy abnormalities and associations with preterm birth(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, studies conducted on large populations in China suggested associations of high triglycerides with hypertensive and large-for-gestational aged neonates(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe outcome of neonates likely to be affected by maternal lipid levels is neither limited to preterm birth, low birth weight, macrosomia, nor hypoglycemia, but also to jaundice(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). There are studies, nonetheless, wherein maternal hypertriglyceridemia and high LDL-C are directly associated with overnutrition and macrosomia in infants(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Conversely, the presence of maternal dyslipidemia may also be considered as an indication of an underlining metabolic problem, for instance, insulin resistance, thereby increasing the likelihood of hypoglycemia and jaundice in newborn infants, as often manifested in infants born to diabetic women. According to Smith et al. (2018), pregnant women with dyslipidemia are more likely to have premature deliveries, suggesting thereby an association between pro-atherogenic dyslipidemia and placental insufficiency leading to premature births(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In addition, on the adverse effects of pregnancy, a cross-sectional study in Iran found pregnant women with hyperlipidemia, particularly those with high TG and cholesterol, are at greater risk for neonatal adverse outcomes, namely, neonatal ICU admission and low Apgar score(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In mechanism, maternal dyslipidemia may underlie the high oxidative and inflammation level within the intrauterine setting, thereby affecting placental and fetal development(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn spite of this, there is very limited literature from sub-Saharan Africa, including Uganda, on the childhood prevalence of maternal dyslipidemia and its neonatal outcomes(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Traditionally, in many low and middle-income countries, including those in sub-Saharan Africa, routine lipid level measurements are still not part of antenatal care, unless in situations of undernutrition and infectious disease risk in pregnancy. There is only very limited attention given to cardiovascular disease risk factors during pregnancy in resource-constrained settings, and only recently has this changed. In lactating women in Ghana, for example, dyslipidemia is known to be very high, with more than half of lactating women either having high levels of LDL and low levels of HDL(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This does, therefore, draw attention to the fact that other than cardiovascular disease, metabolic risk factors are still high in the African lactating woman\u0026rsquo;s populace. But, as per our briefing, no valid research as yet explored maternal blood lipid levels and neonates within the Ugandan hospital setting, as at the date of this study(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Kayunga Regional Referral Hospital (KRRH)\u003c/b\u003e covers a vast majority of the peri-rural and rural populations in central Uganda, where the magnitude of dyslipidemia and non-communicable conditions in pregnancy is yet undetermined. Determining the magnitude of maternal dyslipidemia and the level at which it influences pregnancy is crucial for the determination of interventions and measures to address the issue. Our study hypothesized that dyslipidemia is common among women within the study setting and influences the rate of adverse neonatal outcomes.\u003c/p\u003e \u003cp\u003eThe aim of this research was, therefore, to establish the level of dyslipidemia among women in the postnatal ward within the KRRH, to provide insights into what the adverse outcomes are for neonates whose mothers are dyslipidemic, and to establish the essential maternal variables associated with this level of dyslipidemia. In doing so, this research will provide the necessary evidence to establish whether universally routine screening and prevention for dyslipidemia is required within this setting.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThe study was a cross-sectional survey and was carried out in Kayunga Regional Referral Hospital (KRRH) in central Uganda. The hospital is a referral center and is mainly serviced by several other nearby districts, including Kayunga, Kamuli, Nakasongola, and parts thereof in Mukono. The maternity facility in the hospital handles general as well as complicated cases of obstetrics, and the institution also has a post-delivery ward where the mother is supposed to spend at least 24 to 48 hours after giving birth. The study was carried out for 4 months, ranging from May to September 2025, within which the study coincided with general provision of maternity health to women.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Sampling\u003c/h3\u003e\n\u003cp\u003eThe proposed study will target women who have just given birth and are admitted in the postnatal ward at KRRH.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e were all women in the postnatal unit, whether by vaginal or Caesarean section, in the study period and those who gave informed consent to participate\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e were women who did not give consent, and any mother known to have had pregnancy-associated lipid abnormalities, as well as those on any lipid-lowering agent. Postpartum women who were so critically ill they were not able to be interviewed and provide blood were also excluded from this study.\u003c/p\u003e \u003cp\u003eThe consecutive sampling technique was used, whereby all eligible women were recruited into the study until the required sample size was achieved.\u003c/p\u003e\n\u003ch3\u003eStudy Sampling\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample size determination\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eFirst objective\u003c/strong\u003e \u003cp\u003e(Daniel, 1999) formula was used to calculate the sample size\u003c/p\u003e \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1766673397.png\" style=\"width: 154px;\"\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;needed sample size estimate\u003c/p\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;critical value for normal distribution at 95% confidence level, corresponding to 1.96\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;estimated prevalence rate, which is 66.7% from a study done in Ethiopia (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHence,\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1766673474.png\" style=\"width: 290px;\"\u003e\u003c/p\u003e\n\u003cp\u003eAdded by 10% for non-responses Final Sample Size Therefore to achieve all objectives I take the maximum sample size \u003cb\u003e376\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eThe data were collected by the principal investigator along with the research assistant through face-to-face interviews and chart reviews in the postnatal ward, usually on the first day after delivery. The structured format of the questionnaire allowed documentation of the maternal sociodemographic factors like age, marital status, educational level, occupation, and family income, as well as pregnancy-related details like gravidity, parity, antenatal attendance, and any pregnancy-related complications. The medical history recorded included height and pre-pregnancy weight of the woman to calculate the BMI, and presence of any chronic conditions like hypertention, diabetes, gestational diabetes, and pre-eclampsia. Lifestyle-related issues like the exposure of the mother to smoking, and alcohol and her level of physicial activity, where the lack of physicial activity was described as any activity other than regular or predominantly sedentery activity.\u003c/p\u003e \u003cp\u003eThe antenatal data were abstracted from ante-natal cards or remembered by the participant. Pre-pregnancy body mass index was defined by WHO standards. The first ante-natal weight recorded was used if there was no recorded pre-pregnancy weight. Blood pressures and hospital diagnoses were used to define pre-eclampsia and Diabetes.\u003c/p\u003e \u003cp\u003eVenous blood samples (5mL) were obtained for the determination of the lipid profile, ideally after an overnight fast, but this was not feasible in all cases, especially after partum. The specimen was processed within an hour, and the analysis was done on an automated analyzer (Cobas 6000). The lipids assessed were TC, LDL-C, HDL-C, and TG. Laboratory abnormalities were compared to standard thresholds, and dyslipidemia was defined as the presence of any of the abnormalities.\u003c/p\u003e \u003cp\u003eNeonatal information was collected through maternity and newborn files, which included gestational age, birth weight, Apgar score, newborn complications, and NICU admissions. The primary outcomes were preterm birth, low birth weight, macrosomia, neonatal hypoglycemia, jaundice, and early neonatal mortality. The above-mentioned outcomes were compared between women with and those without dyslipidemia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData was entered into Microsoft Excel 2016 and processed with the aid of IBM SPSS software, version 20, after thorough cleaning for completeness and accuracy. The data was described using statistics, with categorical variables described in frequencies and percentages, while continuous variables were described by means and standard deviations or median and interquartile range, as appropriate. \u003cb\u003eThe prevalence of dyslipidemia\u003c/b\u003e in mothers and various lipid abnormalities and their association with neonatal outcomes was determined using 95% confidence intervals. Comparison of categorical variables was conducted by Pearson\u0026rsquo;s chi-square test or Exact Test, while odds ratio with 95% confidence interval determined associations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFactors associated with maternal dyslipidemia\u003c/b\u003e were assessed initially by performing bivariate logistic regression analysis. The variables were considered for multivariate logistic regression analysis, and backward stepwise procedure, if p\u0026thinsp;\u0026lt;\u0026thinsp;0.20. Predictors were declared significant for the model if p was less than 0.05, and the adjusted odds ratios with 95% confidence interval were used. The model\u0026rsquo;s goodness of fit was tested with Hosmer \u0026amp; Lemeshow\u0026rsquo;s test, and the level of significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical consideration\u003c/h3\u003e\n\u003cp\u003e The research was granted ethical clearance by the Kampala International University Ethics Committee, and it also received approval from the Kayunga Regional Referral Hospital and the Uganda National Council for Science and Technology. The research was conducted on volunteers who were freely informed about the research objectives and procedures and were required to provide their voluntary and written-informed consent. The research ensured the participants\u0026rsquo; privacy by assigning them codes, as opposed to names, and did not carry any financial rewards, but they were, however, granted free lifestyle and lipid tests. The research was governed by all applicable human research standards\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of \u003cb\u003e376\u003c/b\u003e postpartum women were recruited, with an overall participation rate of over 95%. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describe the participants' socio-demographic profiles. The median age was 29 (range 16\u0026ndash;44) years. The majority were married (75.8%) and educated, though 23.4% never attended school. Unemployment was high (41.8%); the remainder were self-employed or formally employed. The parity level was high, with 53.5% and 25.8% giving 3\u0026ndash;5 and greater than five previous pregnancies, respectively, and also reflective of 25.3% with more than five total live births (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pregnancy-related complications were gestational diabetes (26.9%) and preeclampsia (23.1%). The lifestyle attributes defined low rates of current smoking (3.2%); nonetheless, 33.8% drank either during pregnancy or PPD. The majority, 72.3%, did no special diet, centered on high-carbohydrate traditional foods. Physical inactivity was apparent, as 34.3% did no exercises, and only 45.2% exercised regularly. Family history of cardiovascular disease was yes in 24.2%. Additionally, 23.7% and 17.6% tested high for blood pressure and have been told they have been told they have diabetes, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSocio-demographic characteristics of study participants\u003c/b\u003e \u003cem\u003eCaption: Socio-demographic characteristics of postpartum women enrolled at Kayunga Regional Referral Hospital (KRRH). Frequencies and percentages are used to display values.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequencies (N\u0026thinsp;=\u0026thinsp;376)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentages (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500,000/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500,000/- to 1,000,000/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1,000,000/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMaternal health and lifestyle characteristics.\u003c/b\u003e \u003cem\u003eCaption: Maternal clinical and lifestyle characteristics of postpartum women at KRRH, including BMI, obstetric history, chronic conditions, and lifestyle behaviors\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequencies (N\u0026thinsp;=\u0026thinsp;376)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentages (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of pregnancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of live births\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes during pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-eclampsia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsumes alcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow a special diet during pregnancy or postpartum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of cardiovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother diabetic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePrevalence of Maternal Dyslipidemia\u003c/p\u003e \u003cp\u003eMore than half of the women in this study had an abnormal lipid profile, indicating maternal dyslipidemia. The incidence of maternal dyslipidemia was 58.2% (219 women out of 376, 95% confidence interval 53.2% to 63.2%). This is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which indicates that three out of five women presenting in the postpartum period have at least one type of dyslipidemia. The other 41.8% (157 women out of 376) were all normallipidemic, and this is an indication of the high prevalence of abnormalities in maternal lipids, even among those women without known antepartum metabolic diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cem\u003eillustrates the spread of Lipid Abnormalities among the study participants. Low levels of high-density lipoprotein (HDL) were the most prevalent, affecting 71.5% of women. High levels of triglycerides and low-density lipoprotein (LDL) were also common, affecting 39.9% and 41.2% of women, respectively. High total cholesterol was the least prevalent, affecting 21.8% of women. Most women presented with two or more abnormalities, low HDL co-present with high TG or high LDL being very common. The confidence intervals showed high accuracy, ranging from 69.6% to 78.4%\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypes of lipid abnormalities commonly observed\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid Abnormality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequencies (N\u0026thinsp;=\u0026thinsp;376)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentages (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.6\u0026ndash;78.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.6\u0026ndash;30.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.1\u0026ndash;64.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;150(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.7\u0026ndash;43.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;130 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.5\u0026ndash;60.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;130 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.4\u0026ndash;43.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.9\u0026ndash;80.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;200 mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.8\u0026ndash;22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eResults on maternal lipids revealed a high prevalence of dyslipidemia, with low levels of the protective high-density lipoprotein, known as HDL, or \u0026ldquo;good\u0026rdquo; cholesterol. The average level of HDL in dyslipidemic women was 38.7 mg/dL, with a standard deviation of \u0026plusmn;\u0026thinsp;6.1. The high prevalence of triglycerides and LDL-cholesterol, the types associated with increased risk, was shown by high average levels of 162.4 mg/dL and 128.7 mg/dL, with standard deviations of \u0026plusmn;\u0026thinsp;54.3 and \u0026plusmn;\u0026thinsp;36.5, respectively. The level of hypercholesterolemia (TC\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL) was found to be 21% among women, with an average level of total cholesterol of 188.9 mg/dL and a standard deviation of \u0026plusmn;\u0026thinsp;44.0.\u003c/p\u003e \u003cp\u003eThe presence of maternal dyslipidemia was significantly associated with various unfavorable neonatal outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). There was an increased incidence of preterm birth in women with dyslipidemia (21.9%) as compared to those with normolipidemia (9.6%) (χ\u0026sup2; = 9.15, p\u0026thinsp;=\u0026thinsp;0.0025). Neonatal hypoglycemia was significantly associated, with 84.2% cases being from the dyslipidemic group (p\u0026thinsp;=\u0026thinsp;0.018). The incidence of neonatal jaundice requiring treatment was significantly high in the infants of dyslipidemic women (44.3% as compared to 22.9% in normolipidemic women; χ\u0026sup2; = 17.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, NICU admissions, early neonatal deaths, and low 5-minute Apgar scores were similar in both groups. The incidence of macrosom\u003c/p\u003e \u003cp\u003eIn general, maternal dyslipidemia is strongly associated with prematurity, neonatal hypoglycemia, and jaundice, and this is likely due to the metabolic and placental effects of dyslipidemia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelation between maternal Dyslipidemia and Adverse Neonatal Outcomes among Women Admitted in the Postnatal Unit at KRRH.\u003c/b\u003e \u003cem\u003eCaption: Maternal dyslipidemia and neonatal outcomes are related. Preterm birth, neonatal hypoglycemia, and jaundice were all substantially correlated with dyslipidemia (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMaternal dyslipidaemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChi-square (χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003e157(41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003e219(58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreterm (n\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.0025*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrosomia (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoglycemia (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCOR\u0026thinsp;=\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaundice (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICU Admission (n\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly Neonatal death (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Apgar (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Statistically significant, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, approximately 35% of babies born to dyslipidemic mothers required specialized care (NICU admission or treatment for complications) compared to 28% born to normolipidemic mothers, although this composite difference was not statistically tested. Interestingly, although there were more premature births among dyslipidemic mothers, birth weights in term infants did not significantly differ-implying that maternal hyperlipidemia did not result in a higher risk of macrosomic infants in this population. This is in contrast to several reports that have associated maternal hypertriglyceridemia with fetal overgrowth[4], but in our setting other factors may have blunted that effect, such as the predominance of younger mothers or undernutrition in some women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with Maternal Dyslipidemia\u003c/h2\u003e \u003cp\u003e \u003cb\u003eBivariate analysis\u003c/b\u003e examined maternal sociodemographic, medical, obstetric, and lifestyle variables associated with dyslipidemia (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Several of these variables had strong unadjusted associations. There was a clear gradient in education: mothers who had no formal education had substantially higher dyslipidemia prevalence, 70.5%, compared to those with tertiary education, 43.7%, yielding a crude odds ratio of 3.08 (95% CI: 1.68\u0026ndash;5.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Primary education also had higher odds, COR\u0026thinsp;=\u0026thinsp;1.91, p\u0026thinsp;=\u0026thinsp;0.02. These findings suggest that low educational attainment, perhaps due to reduced access to health information, low income, or limited health literacy, independently contributes to poor lipid profiles.\u003c/p\u003e \u003cp\u003eMarital status was borderline significant, with married women showing a somewhat higher prevalence of dyslipidemia than single women: 61.1% versus 49.5%, respectively (COR\u0026thinsp;=\u0026thinsp;1.60, p\u0026thinsp;=\u0026thinsp;0.051). Higher household income (\u0026gt;\u0026thinsp;1,000,000 UGX per month) was surprisingly associated with greater odds of dyslipidemia compared to lower income (\u0026lt;\u0026thinsp;500,000 UGX), COR\u0026thinsp;=\u0026thinsp;2.23 (p\u0026thinsp;=\u0026thinsp;0.0058). The pattern may reflect dietary transitions associated with higher socioeconomic status, including increased consumption of processed and calorie-dense foods.\u003c/p\u003e \u003cp\u003eDyslipidemia was strongly associated with medical and obstetric factors. Prepregnancy obesity was a strong predictor: 85.7% of obese women were dyslipidemic, as compared with approximately half of normal-weight women (COR\u0026thinsp;=\u0026thinsp;5.85, p\u0026thinsp;=\u0026thinsp;0.0055). High gravidity and parity also increased risk. More than five pregnancies conferred COR\u0026thinsp;=\u0026thinsp;2.46 (p\u0026thinsp;=\u0026thinsp;0.0044), and more than five live births conferred COR\u0026thinsp;=\u0026thinsp;3.29 (p\u0026thinsp;=\u0026thinsp;0.0003), suggesting cumulative metabolic strain from successive pregnancies. Pregnancy complications showed striking associations with dyslipidemia. Gestational diabetes increased prevalence to 73.3% with COR\u0026thinsp;=\u0026thinsp;2.45 (p\u0026thinsp;=\u0026thinsp;0.001) and that of pre-eclampsia to 74.7% with COR\u0026thinsp;=\u0026thinsp;2.59 (p\u0026thinsp;=\u0026thinsp;0.015), consistent with well-known metabolic disturbances including insulin resistance, vascular dysfunction, and abnormalities of lipid metabolism.\u003c/p\u003e \u003cp\u003eIn fact, some of the most potent associations were captured for lifestyle factors. Though not as common, cigarette smoking was associated with a higher prevalence of dyslipidemia, 75% versus 57.7% (COR\u0026thinsp;=\u0026thinsp;2.20, p\u0026thinsp;=\u0026thinsp;0.038). Alcohol use was also associated with higher prevalence of dyslipidemia, 71.7% versus 51.4% (COR\u0026thinsp;=\u0026thinsp;2.39, p\u0026thinsp;=\u0026thinsp;0.017). Physical inactivity emerged as a major risk factor, as 70.5% of women who never exercised were found to be dyslipidemic (COR\u0026thinsp;=\u0026thinsp;5.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a dose-response trend across those exercising rarely, 62.4% (COR\u0026thinsp;=\u0026thinsp;4.15, p\u0026thinsp;=\u0026thinsp;0.012). Neither family history of cardiovascular disease nor baseline hypertension demonstrated strong crude associations, though elevated blood pressure fulfilled criteria for inclusion in multivariable analysis.\u003c/p\u003e \u003cp\u003eOverall, the bivariate analysis highlighted many variables, especially obesity, high parity, gestational diabetes, pre-eclampsia, smoking, alcohol use, and low physical activity, to be significantly associated with maternal dyslipidemia. These were then carried forward into multivariable logistic regression to control for confounding.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate regression analysis of the factors associated with maternal Dyslipidemia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMaternal Dyslipidemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003e157(41.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003e219(58.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(57.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21(0.6812\u0026ndash;2.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(38.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39(0.795\u0026ndash;2.4497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49(60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35(0.7379-2.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3316*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111(38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174(61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60(0.996\u0026ndash;2.576)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62(70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.08(1.67-5.9265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0008*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.906(1.0406-3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0367*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.67(0.9155\u0026ndash;3.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72(45.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35(0.849\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31(0.756\u0026ndash;2.2733)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500,000/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500,000/- to 1,000,000/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73(56.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.727\u0026ndash;1.835)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1,000,000/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(71.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23(1.262\u0026ndash;3.956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-pregnancy BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.975(0.562\u0026ndash;1.692)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120(49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123(50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37(0.730\u0026ndash;2.555)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85(1.680-20.388)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0055*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of pregnancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38(48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88(43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35(0.800-2.283)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68(70.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46(1.326\u0026ndash;4.5946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0044*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of live births\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112(54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33(0.789\u0026ndash;2.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.29(1.723\u0026ndash;6.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes during pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(73.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.45(1.38\u0026ndash;4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-eclampsia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59(1.50\u0026ndash;4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20(1.60\u0026ndash;6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsumes alcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39(1.54\u0026ndash;3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow a special diet during pregnancy or postpartum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117(43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155(57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21(0.90\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(29.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91(70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.98(3.19\u0026ndash;11.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106(62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.15(2.35\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of cardiovascular disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60(65.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60(0.76\u0026ndash;2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129(45.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156(54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65(73.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.31(1.39\u0026ndash;3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother diabetic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e176(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53(0.88\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Statistically significant, p\u0026thinsp;\u0026lt;\u0026thinsp;0.2\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eIn all, the bivariate results suggested that education, income, obesity, high gravidity, gestational diabetes, pre-eclampsia, smoking, alcohol, and physical inactivity all showed significant or borderline-significant associations with maternal dyslipidemia. Each of these factors was then included in the multivariable model in order to determine those factors acting as independent predictors of maternal dyslipidemia when adjusted for all others.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMultivariable logistic regression\u003c/b\u003e showed that after adjusting for potential confounding factors simultaneously, four factors emerged as independent predictors of maternal dyslipidemia in this population. Adjusted Odd Ratios are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and are considered significant if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePre-pregnancy obesity\u003c/b\u003e remained a strong predictor in that obese women were found to be more than twice as likely to develop dyslipidemia as women of normal weight (AOR\u0026thinsp;=\u0026thinsp;2.17, 95% CI 1.73\u0026ndash;6.31, p\u0026thinsp;=\u0026thinsp;0.04). This agrees with the fact that obesity is often associated with insulin resistance and a pro-atherogenic lipid profile[20]. After adjustment, overweight (AOR 1.25) and underweight (AOR 0.56) were not significant; this reflects a threshold effect since only obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) was associated with significantly higher risk.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePre-eclampsia\u003c/b\u003e in the index pregnancy was independently associated with maternal dyslipidemia: AOR\u0026thinsp;=\u0026thinsp;2.07; 95% CI 1.67\u0026ndash;6.43, p\u0026thinsp;=\u0026thinsp;0.02. This indicates that women with hypertensive disorders of pregnancy were roughly twice as likely to have abnormal lipids, adjusting for BMI and age, among other factors. The pathophysiology of pre-eclampsia includes endothelial dysfunction and oxidative stress, events that might be exacerbated or lead to dyslipidemia.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSmoking\u003c/b\u003e remained an important risk factor, with a fully adjusted odds ratio for dyslipidemia of 2.36 (95% CI 1.93\u0026ndash;4.62, p\u0026thinsp;=\u0026thinsp;0.01) for current smokers versus non-smokers. Thus, even in this minimally smoking population, tobacco use remained a powerful risk factor for abnormal lipids. The effect of smoking is probably explained by the facts that smoking reduces HDL cholesterol and increases LDL oxidation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePhysical inactivity\u003c/b\u003e remained an independent predictor. In particular, mothers who never exercised were almost three times more likely to have dyslipidemia than those who exercised regularly (AOR\u0026thinsp;=\u0026thinsp;2.74, 95% CI 1.63\u0026ndash;7.48, p\u0026thinsp;=\u0026thinsp;0.04). Those who exercised rarely still had higher odds (AOR 1.67) but this was not significant after adjustment (p\u0026thinsp;=\u0026thinsp;0.25), suggesting that the most sedentary behavior-no exercise at all-is the critical risk level. Regular physical activity is likely to confer protective effects on lipid metabolism, as reflected by much lower dyslipidemia rates in that group.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSeveral other factors were entered into the model but did not remain independently associated when controlling for other variables. For example, low education (no schooling) had an AOR of 1.94 (95% CI 0.86\u0026ndash;4.15, p\u0026thinsp;=\u0026thinsp;0.07) compared to tertiary education \u0026ndash; a sizeable increase but not statistically significant in the adjusted model. Household income\u0026thinsp;\u0026gt;\u0026thinsp;1,000,000 UGX was no longer significant (AOR\u0026thinsp;~\u0026thinsp;1.05, p\u0026thinsp;=\u0026thinsp;0.32) when other confounders were considered, perhaps due to its effect being mediated through other lifestyle variables that are associated with income, such as diet. Similarly, gestational diabetes, significant in univariate analysis, did not retain significance in the final model (AOR 1.84, 95% CI 0.93\u0026ndash;5.72, p\u0026thinsp;=\u0026thinsp;0.09), which may have been because many women with GDM also had high BMI or developed pre-eclampsia, which were accounted for. Similarly, parity and alcohol use did not reach significance after adjustment. Of note, the variable for \"maternal history of diabetes\" did not reach statistical significance (AOR 1.49, p\u0026thinsp;=\u0026thinsp;0.36) - while genetic factors are very important, they may play a smaller observable role in the presence of very strong lifestyle and pregnancy-related factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eMultivariate regression analysis of the factors associated with maternal Dyslipidemia among Women Admitted in the Postnatal at Unit KRRH.\u003c/b\u003e \u003cem\u003eCaption: Maternal dyslipidemia's independent predictors were found using multivariate logistic regression. After adjustment, smoking, obesity, pre-eclampsia, and physical inactivity were still significant.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21(0.72\u0026ndash;2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.49\u0026ndash;5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39(0.83\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24(0.37\u0026ndash;4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(1.08\u0026ndash;2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25(0.91\u0026ndash;6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.08(1.68\u0026ndash;5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.94(0.86\u0026ndash;4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91(1.08\u0026ndash;3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29(0.54\u0026ndash;3.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.67(0.45\u0026ndash;2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03(0.73\u0026ndash;5.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-pregnancy BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98(0.58\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56(0.15\u0026ndash;5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37(0.74\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25(0.53\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.85(1.63\u0026ndash;7.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17(1.73\u0026ndash;6.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of pregnancies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35(0.83\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.23(0.62\u0026ndash;4.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.46(0.87\u0026ndash;4.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.91(0.71\u0026ndash;6.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of live births\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.33(0.81\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27(0.35\u0026ndash;4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29(0.87\u0026ndash;6.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.93(0.61\u0026ndash;7.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes during pregnancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45(1.38\u0026ndash;4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84(0.93\u0026ndash;5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre-eclampsia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.59(1.50\u0026ndash;4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.07(1.67\u0026ndash;6.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoker\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.10(1.60\u0026ndash;6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.36(1.93\u0026ndash;4.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConsumes alcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39(1.54\u0026ndash;3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.54(0.87\u0026ndash;2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFollow a special diet during pregnancy or postpartum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21(0.90\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.05(0.67\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of exercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.98(3.19\u0026ndash;11.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.74(1.63\u0026ndash;7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15(2.35\u0026ndash;7.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67(0.78\u0026ndash;4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.31(1.39\u0026ndash;3.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.76(0.54\u0026ndash;3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother diabetic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.53(0.88\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.49(0.73\u0026ndash;2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*statistically significant, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment, the following four variables emerged as being particularly important, and all are related to 2- to 3-fold increases in the odds of dyslipidemia: maternal obesity, pre-eclampsia, smoking, and lack of exercise. These are likely the main target for intervention. The relationships represented by education and gestational diabetes, as evident from crude models, are accounted for by and fail to reach significance in the presence of other variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study involving 376 women in the postpartum period in a regional hospital in Uganda, we identified a high prevalence of maternal dyslipidemia and significant associations with certain neonatal outcomes and maternal risk factors. It appears that this is one of the first studies to have assessed maternal lipid profiles and pregnancy outcomes in this manner in Uganda, and implications for practice are significant, as they pertain to low resource settings.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of dyslipidemia\u003c/h2\u003e \u003cp\u003eWe found that 58.2% of the mothers aged under 30 were dyslipidemic (at least one value was abnormal). This value is much higher than the previous global estimates of dyslipidemia during pregnancy, ranging from 15% to 40%(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Our value does, however, appear consistent with the more recent literature from low and middle-income countries. Thus in Ghana found over half the lactating women to have hypercholesterolemia or low HDL. Likewise(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), in China, found the prevalence of dyslipidemia to reach 53.7% within one month of delivery, and increase with raised BMI(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The close similarity between our findings and those of other studies indicates that dyslipidemia is becoming increasingly common within women of childbearing age in all parts of the globe, likely in response to global lifestyle and diet trends. Also, note the remarkably high proportion (.72% of our group) of low HDL-C. Low HDL is commonly associated with high prevalence rates of MetS, VA, and high-carb diet. Notwithstanding the low intake of animal fats, the diet in rural Uganda is high in carbohydrates and low in dietary fats, which may underlie low levels of HDL-cholesterols. On the other hand, the physiological hyperlipidemia of late pregnancy may as yet not be entirely resolved by the immediate puerperium, thereby artificially inflating the percentages of abnormalities(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Also, pregnancy-related abnormalities, including volume and hormonal effects, may play significant parts in artificially altering levels of lipids immediately following delivery.\u003c/p\u003e \u003cp\u003eOur study\u0026rsquo;s lipid profile, dominance by low levels of HDL and high levels of LDL/TG, reflects a status reminiscent of those within the South Asian and African populations, as opposed to those within the Western populations. In point of fact, within Ethiopia, it was shown that both TG and cholesterol levels were increasingly high in pregnancy, and indeed, many women were found to be above the normal cut-off thresholds by the third trimester(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The large percentage of women in our Ugandan study with dyslipidemia confirms the point that we are dealing with both issues of undernutrition and those pertaining to overnutrition within our maternal population.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNeonatal Outcomes\u003c/strong\u003e \u003cp\u003eIn this analysis, we identified that maternal dyslipidemia significantly increased the incidence of preterm birth. The prevalence of preterm birth in dyslipidemic women is 21.9%, which is well over twice the incidence in women with normal lipids (9.6%). This significant association persisted after controlling for the confounding variables, suggesting that dyslipidemia, or factors associated with it, play some part in mechanisms of preterm labor and medically indicated early delivery. The mechanism may be hypothesized as follows that is, the \u0026lsquo;atherosclerosis\u0026rsquo; of the uteroplacental vascular bed, secondary to high levels of LDL and triglicerides, may establish placental insufficiency, causing preterm delivery(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Another mechanism may propose dyslipidemia as \u0026lsquo;marker\u0026rsquo; for other undiagnosed cases of maternal metabolic syndromes and undiagnosed cases of Diabetes, both well recognized to increase rates of preterm deliveries due to spontaneous term and induced preterm deliveries (in preeclampsia, for example). Our observation is supported other studies, wherein Jiang et al. carried out this meta-analysis and reported in 2017, as follows\u0026mdash;that is, \u0026lsquo;maternal hypertriglyceridemia during pregnancy significantly increased the risk of preterm birth, with an odds ratio of overall 1.5 to 2'(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In similar observation conducted on Nigeria by \u0026lsquo;Ottun et al. multicentric cohort studies, 2022,\u0026rsquo; they have now been able to show as under\u0026mdash;that is, \u0026lsquo;Hyperlipidemia is significantly associated with increased incidence of spontaneous preterm delivery'(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our observations, thereby, add to observations obtained from East-African studies on this subject, indicating the significant correlation and doubly important contribution of dyslipidemia as \u0026lsquo;risk factors\u0026rsquo; for \u0026lsquo;prematurity,\u0026rsquo; and hence, important implications within the context of reducing neonatal mortality presenting as \u0026lsquo;preterm birth.\u0026rsquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003eNotably, we did not establish any significant relationship between maternal dyslipidemia and macrosomic birth within our study population. This may seem contrary to other studies, which have shown that high maternal cholesterol and triglycerides are both risk factors for macrosomic birth because of the resultant overgrowth and high birthweight of the baby(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). For instance conducting a study within Iraq, found high TG and LDL within late gestation and high chances of giving birth to macrosomic babies(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The incidence of macrosomic birth in our study did not show any variation based on the status of the mother\u0026rsquo;s dyslipidemia. The reason here may be the overshadowing effect of controlling blood sugar on macrosomic birth, as opposed to other factors like dyslipidemia. Many of the women within our study were either obese or suggested cases of metabolic syndromes, yet they were all not giving birth to large babies. Possibly, this may be an indication of other factors within this population, including low intake and genetic issues, hindering growth within fetuses, as evidenced by low birthweight within dyslipidemic women, likely linked to the high proportion of preterm deliveries within this group. Yet, an important consideration here may be linked to the point in the woman\u0026rsquo;s life where lipids were measured, as it was carried out after birth, and the main growth within the fetus may be linked to measurements and determination within late-third gestations. Some women may likely develop high blood lipids following child birth, likely linked to the point of initiation within lactations and high fat reserve mobilization within this same lactating phase, and this will, therefore, play no significant part in influencing growth within fetuses. Our conclusion, therefore, may likely show otherwise, within this Ugandan setting, than within other populations, as they seemingly show significant predictable relationships within dyslipidemia and macrosomic birth within other environments.\u003c/p\u003e \u003cp\u003eWe did not observe any significant effect of dyslipidemia on Apgar score, NICU admission, and early neonatal deaths after adjusting for the presence of prematurity. This indicates that, except for the mentioned metabolic issues, the overall newborn health status, as affected by dyslipidemia, may have less significance at birth or is possibly confounded by gestational age. The early neonatal deaths among our cohort were largely restricted to either extreme preterm and infectious cases, in whom the mother\u0026rsquo;s lipid level is probably not crucial.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMaternal risk factors for dyslipidemia\u003c/b\u003e: Our multivariable analysis identified four independent risk factors: obesity, pre-eclampsia, smoking, and physical inactivity. All of these are well-aligned with existing knowledge, lending credibility to our findings. Pre-pregnancy obesity emerged as one of the strongest predictors, which is expected since obesity is characterized by insulin resistance and an atherogenic lipid profile (high TG, high LDL, low HDL).\u003c/p\u003e \u003cp\u003eThis is in line with results from other studies; for instance demonstrated that higher BMI in lactating women was associated with significantly greater odds of dyslipidemia(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Similarly, a study in Ethiopia's general population found obesity to correlate strongly with dyslipidemia prevalence(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In our context, with 41% of women overweight or obese, the rising trend of maternal obesity foretells a concurrent rise in dyslipidemia. Public health measures to reduce obesity in women of reproductive age could thus have the added benefit of improving lipid profiles and possibly pregnancy outcomes.\u003c/p\u003e \u003cp\u003eOf particular note is the association of pre-eclampsia with dyslipidemia. Pre-eclampsia has been referred to as a \u0026ldquo;cardiovascular accident\u0026rdquo; of pregnancy and shares risk factors with CVD, including dyslipidemia. We found a two-fold higher odds of dyslipidemia in women with pre-eclampsia. This is in line with the notion that endothelial dysfunction and oxidative stress in pre-eclampsia may be exacerbated by high levels of LDL and triglycerides(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In fact, hypertriglyceridemia is thought to play a role in the pathogenesis of pre-eclampsia by contributing to small dense LDL formation and endothelial damage. study reported that pregnant women with hyperlipidemia had increased risk for developing pre-eclampsia(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Our findings support this bidirectional link: pre-eclamptic women should perhaps be evaluated for metabolic risk factors, and conversely, women with significant dyslipidemia might warrant closer blood pressure surveillance in pregnancy.\u003c/p\u003e \u003cp\u003eMany lifestyle factors were clearly implicated. Smoking doubled the odds of maternal dyslipidemia. The negative impact of smoking on lipid profiles was well known in non-pregnant populations, particularly through reducing HDL-C and raising LDL oxidation(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Our study indicates this holds true for pregnant and postpartum women as well. Clinical implications point to the reinforcement of smoking cessation programs targeting pregnant women. Decreasing smoking may improve not only pregnancy outcomes but also maternal lipid and cardiovascular health.\u003c/p\u003e \u003cp\u003eAnother independent predictor was physical inactivity. Women who did not exercise at all had approximately 2.7 times higher odds for dyslipidemia.\u003c/p\u003e \u003cp\u003ePhysical activity is known to enhance HDL levels and improve overall lipid metabolism. Even moderate exercise during pregnancy can blunt the increase in triglycerides and enhance insulin sensitivity. That inactivity remained significant even after adjusting for obesity suggests that exercise has benefits beyond weight control-perhaps through an enzymatic improvement in lipid profiles, including upregulation of lipoprotein lipase. Our data thus support studies among diverse populations showing a correlation of sedentary behavior with dyslipidemia(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This suggests that encouraging regular physical activity during pregnancy, tailored to maternal tolerance, may be an important intervention in preventing dyslipidemia. Culturally appropriate exercises and antenatal exercise classes could thus be useful in our setting.\u003c/p\u003e \u003cp\u003eSome factors were not retained in the multivariate model but are worth mentioning. Low education and low income were related to higher dyslipidemia in a crude analysis presumably due to poor health literacy and fewer means to support a healthy diet or lifestyle. In the Ghana study, low education was one of the predictors of low HDL status in women(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). While education itself fell out of our final model, lifestyle variables are often intertwined with education; therefore, health education focused on less-educated mothers could have indirect benefits on dyslipidemia. We also found gestational diabetes to be associated with dyslipidemia in bivariate analysis, reflecting other studies; for example, study found GDM mothers had 5.6-fold higher odds of dyslipidemia(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The impact of GDM appears mediated by obesity and other variables in our cohort, as it lost its significance in the full model. However, the high prevalence of GDM suggests that this particular group of individuals may serve as a metabolic subgroup at high risk. Clinicians should consider checking lipid profiles in women diagnosed with GDM as part of a comprehensive assessment of cardiovascular risk(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStrengths and limitations: This study has identified an under-investigated aspect in a low-resource African setting and hence contributes to local evidence on a global problem. We had a fairly large sample size and data on a wide range of variables were collected in a systematic way, enabling multivariable adjustment to isolate independent effects. The use of hospital laboratory assays for lipid profiling adds reliability to the biochemical data, and we followed standardized definitions for outcomes and exposures, enhancing the reproducibility of our work.\u003c/p\u003e \u003cp\u003eHowever, the study also has important limitations. First, its cross-sectional design limits causal inferences. We measured postpartum lipid levels, after the occurrence of neonatal outcomes, which complicates any direct cause-effect interpretation. While it is likely that maternal lipid status in late pregnancy was correlated with what we measured postpartum, acute peripartum changes may influence lipid levels. For instance, the stress of labor or fasting during labor may transiently alter the value of lipids. Serial measurements during the course of pregnancy, if possible, would provide a clearer picture of the timing and impact of dyslipidemia. Secondly, given that this is a hospital-based study, we must note the possibility that our findings may not be generalizable to all pregnant women in the community, particularly those who do not deliver in health facilities. At a minimum, the women in our study delivered at a regional referral hospital, which could indicate some level of access to care; women delivering at home or at smaller centers might differ in their risk profile. Thirdly, we did not obtain detailed dietary intake data-e.g., fat, sugar, or micronutrient consumption-which would be an important confounder of lipid levels. There is no doubt that diet during pregnancy and the puerperium affects lipid metabolism, and future studies should include a nutritional assessment. Fourth, we relied on single measurements of lipids and glucose (we did not systematically perform oral glucose tolerance tests to identify all GDM cases); hence, some misclassification is possible-e.g., some dyslipidemic mothers may have had undiagnosed hyperglycemia. Fifth, we did not do advanced lipid testing-like Apo lipoproteins or particle size analysis-which might better characterize risk; we kept to conventional lipid panels available in our setting.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImplications\u003c/strong\u003e \u003cp\u003eDespite limitations, our study highlights a few actionable points. The high rate of dyslipidemia among postpartum women indicates the need to consider routine lipid screening as part of antenatal or postnatal care, especially in the presence of risk factors. Presently, antenatal guidelines in Uganda do not include lipid testing, focusing more on infections, anemia, and hypertensive disorders. Given the findings, integrating a simple lipid profile (that has become more affordable) could help identify at-risk women who might benefit from dietary counseling or closer monitoring. In addition, the associations with smoking and inactivity suggest that existing antenatal education should be extended to also cover lifestyle counseling for nutrition, exercise, and smoking cessation. Such interventions are inexpensive and may improve lipid levels and overall pregnancy health. For example, moderate exercise could be recommended for pregnant women (when there are no contraindications) and could be included in antenatal classes. Nutritional counseling should highlight balanced diets with healthy fats (sources of omega-3) and fiber that might improve HDL and lower LDL.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIt also suggests that the link between dyslipidemia and preterm birth and neonatal complications like hypoglycemia means that women identified with high lipid levels may need a higher intensity of surveillance during pregnancy, including potentially earlier or more frequent ultrasound monitoring for fetal growth and well-being, and preparation for possible preterm delivery, such as offering antenatal corticosteroids if risks become apparent. Neonatal caregivers should be informed about all metabolic risk factors in a newborn's mother so they can proactively manage and monitor issues related to blood sugar and bilirubin.\u003c/p\u003e \u003cp\u003eOur findings are also in agreement with the concept that pregnancy can act as a \u0026ldquo;stress test\u0026rdquo; for future maternal health. Pregnancy dyslipidemia, particularly when occurring in conjunction with other complications such as pre-eclampsia or GDM, is an independent risk factor for cardiovascular disease in later life.\u003c/p\u003e \u003cp\u003eThis should be an opportunity for follow-up after delivery: such mothers should be counseled and possibly enrolled into non-communicable disease prevention programs postpartum. Such interventions include weight management, healthy diet, physical activity, and smoking cessation; these offer dual benefits to prevent not only future cardiovascular disease in the mother but also improve outcomes of any subsequent pregnancies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMore than half of the postpartum women in our study had abnormal lipid profiles, highlighting that maternal dyslipidemia is a serious health concern even in the setting of a regional Ugandan hospital. Low HDL cholesterol was extremely common, and a sizable fraction of women also had high levels of LDL, triglycerides, or total cholesterol. Maternal dyslipidemia was associated with increased risks of preterm birth and neonatal complications such as hypoglycemia and jaundice, complications with the potential to have serious short- and long-term sequelae for the child. We identified pre-pregnancy obesity, pregnancy-related hypertensive disorders, cigarette smoking, and physical inactivity as key modifiable factors associated with dyslipidemia in these women.\u003c/p\u003e \u003cp\u003eThese findings emphasize the urgent need for greater attention to be paid to cardiovascular and metabolic health in antenatal and postnatal care. Routine lipid screening in pregnancy (or early postpartum) could facilitate the early identification of women who are at risk. Coupled with this, lifestyle modification interventions, including dietary improvements, promotion of exercise, and smoking cessation programs for pregnant women, are recommended in an attempt to mitigate dyslipidemia. The importance of addressing maternal dyslipidemia goes beyond improving pregnancy and neonatal outcomes in terms of reduced preterm births and neonatal morbidities but may also provide long-term health benefits by decreasing the mother's future cardiovascular risk. Therefore, maternal dyslipidemia needs to be identified, alongside anemia and infections, as an important component of maternal health that requires surveillance and intervention in resource-poor settings.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRecommendations\u003c/h2\u003e \u003cp\u003eBased on our study findings, we propose the following recommendations for the improvement of maternal and neonatal outcomes related to dyslipidemia.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eInclude Lipid Screening during Antenatal Care\u003c/b\u003e: Health facilities may now consider adding a basic lipid profile test to the care for pregnant women, particularly in mid or late pregnancy, and more especially so in those with risk factors including obesity, advanced maternal age, or history of pre-eclampsia/diabetes. Early detection of dyslipidemia would thus enable timely nutritional and medical interventions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTargeted Nutritional Guidance\u003c/b\u003e: Enhance antenatal and postnatal nutritional education to focus on healthy diets that will improve lipid profiles. This includes increasing fruit, vegetable consumption, and sources of healthy fats, such as fish rich in omega-3, while decreasing intake of processed carbohydrates, sugary drinks, and transfats. Culturally appropriate diet plans need to be developed and, where possible, include family members so they can support the mother's dietary changes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePromote Physical Activity Programs\u003c/b\u003e: Implement community- and facility-based physical activity programs to ensure safe physical activity among women before, during, and after pregnancy. Brief exercises or demonstrations can be given in antenatal clinics. Community health workers need to be trained to counsel pregnant women on active lifestyles as a prevention strategy for excessive weight gain and dyslipidemia.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSmoking Cessation Support\u003c/b\u003e: Screening for tobacco use should be conducted on all pregnant women, with referral to counseling and support for quitting, including behavioral support and connection with cessation programs. Given the strong association between smoking and dyslipidemia, as well as other complications associated with pregnancy, it is of paramount public health policy importance to encourage smoking cessation in pregnancy.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eManagement of identified dyslipidemia\u003c/b\u003e: In women with significantly abnormal lipid levels during pregnancy, closer monitoring and follow-up may be contemplated. While this is not a time for the institution of lipid-lowering medication (such as statins, which are contraindicated), such women should be followed in a high-risk clinic setting. Postpartum follow-up should be arranged to reassess lipid levels and start medical therapy if indicated after breastfeeding, to protect long-term health.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEnhanced Care for Vulnerable Neonates\u003c/b\u003e: Neonatal teams need to be prepared for babies born to mothers with dyslipidemia or related metabolic conditions. These infants would require earlier blood glucose screening for hypoglycemia and more timely bilirubin screening. Maternity services could have protocols such that maternal metabolic flags can automatically trigger neonatal precautionary measures, such as feeding support to avoid hypoglycemia and early jaundice assessment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCommunity Awareness and Lifestyle Programs\u003c/b\u003e: At the community level, create awareness that obesity, poor nutrition, and cigarette smoking have implications on pregnancy outcomes as well as being causes of chronic diseases later in life. Community health forums should address the prevention of \"lifestyle diseases\" in young women. The participation of local leaders, coupled with the use of mass media, may alter perceptions to accept active exercise and proper nutrition as crucial for female health.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFurther Research\u003c/b\u003e: Prospective studies on the trend of changes in lipids during pregnancy and their direct causality with outcomes. Further research will also be warranted on cost-effective interventions (like nutritional supplements or specific diets in pregnancy) that may help improve lipid profiles in low-resource settings. Indeed, exploring the genetics that underpin the dyslipidemia in African women could provide another key in the future to personalized approaches.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe recommendations will be multidisciplinary in nature, led by obstetricians along with midwives, nutritionists, public health practitioners, and policy-makers. Working toward healthier mothers and babies through the prevention, early detection, and management of maternal dyslipidemia will help reduce not only neonatal complications but also cardiovascular disease in the future.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e1. \u003cb\u003eTC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e2. \u003cb\u003eLDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eC\u003c/b\u003e\u0026ndash;Low\u0026ndash;Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e3. \u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cb\u003eC\u003c/b\u003e\u0026ndash;High\u0026ndash;Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e4. \u003cb\u003eTG\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e5. \u003cb\u003eGDM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e6. \u003cb\u003eKRRH\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKayunga Regional Referral Hospital\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e7. \u003cb\u003eNICU\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeonatal Intensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003ch2\u003eHuman Ethics and Consent to Participate\u003c/h2\u003e \u003cp\u003e This study received ethical approval from the Kampala International University Research Ethics Committee (KIU-REC-2025-862) and administrative clearance from Kayunga Regional Referral Hospital (KRRH) and was registered with the Uganda National Council for Science and Technology (UNCST). All participants were \u0026ge;\u0026thinsp;18 years and provided written informed consent at the postnatal care visit after a clear explanation of study procedures, ensuring informed and voluntary participation. The study adhered to the ethical principles of the Declaration of Helsinki.\u003c/p\u003e \u003ch2\u003eConsent for Publication\u003c/h2\u003e \u003cp\u003eNot applicable. This manuscript does not contain any individual person's data in any form.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting Interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e \u003ch2\u003eClinical Trial Registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFathi designed and developed the proposal. Sawdo and fathi performed the data collection and entry. MF.Ismail performed the statistical analysis. MF.Ismail drafted the initial manuscript. Tayrab, Rukamba, Intisar and joseph contributed to reviewing and revising the manuscript. The final manuscript was read and approved by all the authors.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe extend our gratitude to the research assistants and participants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset that was utilized in this study is not publicly available due to ethical considerations. Upon reasonable request, the dataset used can be accessed with the permission of the corresponding author Dr. Fathi Abdi Farah (email: [[email protected]](mailto:[email protected]) )\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun L, Gao B, Wang M, Liu Y, Shan Z, Teng W et al. 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PLoS ONE. 2021;16(2 February):1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaliu MA, Salihu A, Mada SB, Owolabi OA. Dyslipidaemia-related cardiovascular risk among pregnant women attending Aminu Kano Teaching Hospital Kano: A longitudinal study. J Taibah Univ Med Sci. 2021;16(6):870\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePreda A, Preda SD, Mota M, Iliescu DG, Zorila LG, Comanescu AC, et al. Dyslipidemia in Pregnancy: A Systematic Review of Molecular Alterations and Clinical Implications. Biomedicines. 2024;12(10):2252.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelegbe GK, Abdullah SJ, Mohammed BS, Dyslipidemias. Prevalence and Associated Factors among Lactating Women in a Lower- and Middle-Income Country, Ghana. Wertz PW. editor J Lipids. 2023;2023:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu L, Xu X, Yu W, Chen L, Zhang S, Li Y, et al. 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Lipids Health Dis 2017 July 5;16(1):132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCibickova L, Langova K, Schovanek J, Macakova D, Krystynik O, Karasek D. Pregnancy lipid profile and different lipid patterns of gestational diabetes treated by diet itself. Physiol Res. 2022;71(2):241\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Maternal dyslipidemia, Lipid profile, Postpartum women, Neonatal outcomes, Preterm birth, Uganda, Cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-8156962/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8156962/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eMaternal dyslipidemia is an emerging public health concern associated with adverse pregnancy and neonatal outcomes, including preterm birth, macrosomia, and neonatal complications. Despite extensive global research, there is limited data from Uganda regarding the burden, determinants, and neonatal implications of maternal lipid abnormalities.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A cross-sectional study was conducted among 376 postpartum women at Kayunga Regional Referral Hospital from May to September 2025. Data on socio-demographic, obstetric, medical, and lifestyle characteristics were collected using structured questionnaires. Blood samples were analyzed for lipid profiles including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Neonatal outcomes were abstracted from clinical records. Bivariate and multivariate logistic regression analyses were performed to identify factors independently associated with maternal dyslipidaemia, with significance considered at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of maternal dyslipidemia was 58.2% (219/376; 95% CI: 53.2%-63.2%). The most common lipid abnormality was low HDL-C (\u0026lt;\u0026thinsp;40/50 mg/dL), observed in 71.5% (269/376) of mothers, followed by elevated LDL-C (\u0026ge;\u0026thinsp;130 mg/dL) in 41.2% (155/376), hypertriglyceridemia (TG\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL) in 39.9% (150/376), and elevated total cholesterol (TC\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL) in 21.8% (82/376). Maternal dyslipidemia was significantly associated with preterm birth (χ\u0026sup2; = 9.15, p\u0026thinsp;=\u0026thinsp;0.0025), neonatal hypoglycemia (p\u0026thinsp;=\u0026thinsp;0.018), and neonatal jaundice (χ\u0026sup2; = 17.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis revealed that pre-pregnancy obesity (AOR\u0026thinsp;=\u0026thinsp;2.17, 95% CI: 1.73\u0026ndash;6.31, p\u0026thinsp;=\u0026thinsp;0.04), pre-eclampsia (AOR\u0026thinsp;=\u0026thinsp;2.07, 95% CI: 1.67\u0026ndash;6.43, p\u0026thinsp;=\u0026thinsp;0.02), current smoking (AOR\u0026thinsp;=\u0026thinsp;2.36, 95% CI: 1.93\u0026ndash;4.62, p\u0026thinsp;=\u0026thinsp;0.01), and physical inactivity (AOR\u0026thinsp;=\u0026thinsp;2.74, 95% CI: 1.63\u0026ndash;7.48, p\u0026thinsp;=\u0026thinsp;0.04) were independent predictors of maternal dyslipidemia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMaternal dyslipidemia is highly prevalent among postpartum women, with low HDL-C being the most frequent lipid abnormality. There is urgent need for routine lipid screening during antenatal care and implementation of lifestyle interventions to mitigate dyslipidemia and improve maternal and neonatal health outcomes.\u003c/p\u003e","manuscriptTitle":"Prevalence, Adverse Neonatal Outcomes, and Factors Associated With Maternal Dyslipidemia Among Women Admitted in the Postnatal Unit at Kayunga Regional Referral Hospital, Uganda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:19:09","doi":"10.21203/rs.3.rs-8156962/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-02T09:32:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176643200931276734471966608754029537030","date":"2026-01-02T07:14:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126707849943737556376791077816523931287","date":"2025-12-30T06:20:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310175458555980841569458775037297961104","date":"2025-12-29T08:32:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-24T07:11:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T16:59:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T07:50:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T07:48:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-11-19T15:32:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2337884-e6fe-478f-ab5b-5acfc5a2ed6c","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T09:19:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 09:19:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8156962","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8156962","identity":"rs-8156962","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}