Glucose Circadian Rhythm Disruption is Associated with Preeclampsia

Glucose Circadian Rhythm Disruption is Associated with Preeclampsia | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Glucose Circadian Rhythm Disruption is Associated with Preeclampsia View ORCID Profile R Bravo , KH Lee , SA Nazeer , J Ashby Cornthwaite , B Sibai , M Fishel Bartal , C Pedroza doi: https://doi.org/10.1101/2025.02.05.25321670 R Bravo 1 The Institute for Clinical Research & Learning Health Care, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for R Bravo For correspondence: Rafael.BravoSantos{at}uth.tmc.edu KH Lee 1 The Institute for Clinical Research & Learning Health Care, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site SA Nazeer 2 Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site J Ashby Cornthwaite 1 The Institute for Clinical Research & Learning Health Care, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site B Sibai 2 Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site M Fishel Bartal 2 Division of Maternal-Fetal Medicine, Department of Obstetrics, Gynecology, and Reproductive Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site C Pedroza 1 The Institute for Clinical Research & Learning Health Care, McGovern Medical School, The University of Texas Health Science Center at Houston , Houston, TX Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Preview PDF ABSTRACT Background There is emerging evidence of an association between circadian rhythms disruption and pregnancy complications. Preeclampsia is a leading cause of maternal death during pregnancy, and the role of circadian rhythms in predicting preeclampsia is not well understood. Objective Our aim was to determine whether glucose circadian rhythm disruption is associated with preeclampsia and can be used to predict this pregnancy disorder. Methods We analyzed a dataset of 92 recruited individuals with Continuous Glucose Monitoring (CGM) data recorded at 24.62 (sd = 4.97) weeks of gestational age. To study rhythmicity, we performed a cosinor analysis using the packages cosinor and cosinor2 , and we calculated the non-parametric circadian rhythm variables using the nparACT package in R. Furthermore, we performed multiple component cosinor analysis to detect internal oscillations and identify glucose postprandial peaks using the package CosinorPy in Python. Results 71 participants (20 women with preeclampsia) had sufficient data for studying glucose circadian rhythmicity and performing cosinor analysis for multiple components to detect the postprandial peaks. We found that all the participants exhibited a significant circadian rhythm in their glucose oscillation. We developed a model including the time difference between the first postprandial peak and the last one, L5 start-time (time at which the five consecutive hours with the lowest average glucose levels start) and age that was predictive for preeclampsia incidence (AUC: 0.80 95%CI: 0.69-0.91, specificity= 0.88, sensitivity = 0.37). Patients diagnoses with preeclampsia from this model had a reduced amplitude (p < 0.05) and less robust (p < 0.05) glucose rhythmicity. Conclusion We conclude that evaluating glucose circadian rhythm during pregnancy may help to an earlier identification of preeclampsia. INTRODUCTION Hypertensive disorders of pregnancy (HDP) can affect both the mother and the fetus, constituting the leading cause of maternal mortality 1 . Preeclampsia, a common pregnancy complication, is defined by a new onset of hypertension (> 140/90 mmHg) on 2 separate occasions after 20 weeks of gestation in previously normotensive individuals 2 . Often accompanied by proteinuria, preeclampsia may present with thrombocytopenia, impaired liver and/or renal functions, or pulmonary edema in the mother (Deepak, El-Balawi, and Harris,. It can also have significant effects on the fetus, including fetal growth restriction, preterm birth, or even intrauterine fetal death 3 . Current estimates indicate that the global incidence is about 4.6%. Although this percentage might vary depending on location, the incidence of preeclampsia is increasing worldwide 4 . The clinical diagnosis of preeclampsia primarily depends on the assessment of blood pressure, urine protein or protein to creatinine ratio. However, this method has been reported as not sufficiently accurate or sensitive, leading to misdiagnosis and, consequently, delayed treatment 5 . Additionally, it has been reported that preeclampsia is closely related to changes in vasoconstriction, endothelial cells dysfunction, coagulation abnormalities, and immune dysfunction 6 . In this regard, most of the physiological variables associated with preeclampsia follow a 24-hour pattern. The pathophysiology of preeclampsia is not fully understood. Interestingly, preeclampsia is associated with gestational diabetes mellitus (GDM), diabetes type I (T1DM), and diabetes type II (T2DM), suggesting that glucose metabolism might be involved in the onset of preeclampsia 7 , 8 . Circadian rhythms are those biological functions that follow a 24-hour pattern. Virtually every physiological function in the body shows a circadian oscillation that synchronizes internal clocks throughout the physiological systems, tissues, and cells. This synchronization is orchestrated by the suprachiasmatic nuclei, which, during nighttime, send signals to the pineal gland to release the indole melatonin and inform the body to perform nocturnal physiological functions 9 . Disruption of internal circadian rhythms, also known as chronodisruption, has been associated with many health impairments, as well as impaired reproductive function and hypertensive disorders of pregnancy like preeclampsia 10 , 11 . In healthy humans and healthy pregnant women, blood pressure follows a 24-hour pattern with higher values during the active phase and lower values during the sleep period. In fact, in healthy pregnant women, blood pressure dips 10-20 % less than daytime values. However, this oscillation is not that evident in patients with preeclampsia because the decrease during the night is less pronounced 12 , 13 . Moreover, circadian rhythms have been reported to be disrupted in women with preeclampsia. Actually, retrospective studies in humans have reported that sleep alterations or rotating shifts at work are related to a higher risk of hypertensive disorders of pregnancy, including preeclampsia 14 – 16 . Furthermore, previous research has demonstrated that alterations in the molecular clock are associated with preeclampsia in both humans and mice 17 . To date, the relationship between glucose circadian rhythms alterations and preeclampsia is limited, and more evidence is needed to assess whether abnormalities in circadian rhythms can predict preeclampsia. Therefore, our goal was to determine whether differences in glucose circadian rhythm, measured through Continuous Glucose Monitoring (CGM), may predict preeclampsia onset in pregnant individuals. METHODS Data In this prospective observational study, 136 participants were initially enrolled between June 2020 and January 2022 for GDM screening at a single-level IV center. Eligibility criteria included age >18 years at ≤ 30 weeks of gestation. Exclusion criteria were known as type 1 or type 2 diabetes mellitus, history of bariatric surgery, or allergy to adhesive materials. For up to 10 days, participants wore a CGM device (G6 Pro CGM, Dexcom Inc, San Diego, CA) placed in blinded mode at the time of the 50-g GCT. During this time participants were asked to keep their usual diet and life schedule. Neither participants nor providers were able to access the CGM readings until the last recruited participant had delivered and all maternal and neonatal outcomes were collected. Further details of the study can be found in the previous publication 18 . Study outcomes The primary outcome was the prevalence of preeclampsia depending on glucose rhythmic pattern. Secondary maternal outcomes were weight gain during pregnancy, induction of labor, HDPs, cesarean delivery, and postpartum complications, such as endometritis, wound infection, or wound dehiscence. As neonatal outcomes we considered a composite adverse neonatal outcome defined as any of the following components: shoulder dystocia, large size for gestational age (LGA; defined as a birthweight of >90th percentile of the expected value according to gestational age using the nomogram by 19 ), respiratory distress (defined as needing at least 4 hours of respiratory support with supplemental oxygen, continuous positive airway pressure (CPAP), or ventilation at the first 24h of life), need for intravenous (IV) glucose therapy, and fetal or neonatal death. We also considered as neonatal outcomes GA at delivery, preterm delivery, 5-minute Apgar score of <7, NICU admission, small for gestational age (defined as birth weight of <10th percentile of the expected value according to GA), neonatal hypoglycemia (a blood glucose level of <40 mg/dL in the first hour of life or < 50 mg/dL after or requiring medical therapy), mechanical ventilation or CPAP, hyperbilirubinemia, hypocalcemia, and hospital length of stay. Chronobiological algorithms and metrics To determine if any of the parameters obtained from glucose oscillation followed a circadian rhythm, we calculated chronobiological variables including MESOR, amplitude, acrophase, interdaily stability (IS), intradaily variability (IV), relative amplitude (RA) and percentage of rhythm using R software (version 4.1.1; R Core Team, 2013; R Foundation for Statistical Computing, Vienna, Austria). Glucose rhythmicity was calculated performing a cosinor analysis for every subject by fitting a sinusoidal function to the experimental variables with R packages cosinor and cosinor2 (cosinor: http://github.com/sachsmc/cosinor , cosinor2: https://github.com/amutak/cosinor2 ). The mathematical expression is where M is the Midline Estimating Statistic Of Rhythm (MESOR), which is the estimate of central tendency of the distribution of values of an oscillating variable, A is the amplitude (difference between the peak and the MESOR) of the function, ω is the angular frequency equal to 2 π / T with T being the period of the rhythm (i.e., T = 24h for circadian rhythms), and φ is the acrophase 20 , 21 . Additionally, the model tests data, for every observation/volunteer, for circadian rhythmicity detecting a 24h pattern when p-value < 0.05 22 . Because the acrophases in the R packages mentioned above were expressed in radians, we converted them into hours for better comprehension. Moreover, to complement the cosinor models we calculated the Percent of Rhythm, which represented the percent of total variance accounted for by the fitted curve. With the package nparACT 23 we additionally calculated the non-parametric circadian rhythm variables consisting of interdaily stability (IS), intradaily variability (IV) and relative amplitude (RA). The IS quantifies the invariability between the days, and provides an indication of the strength of coupling of the rhythm to a supposedly stable environmental zeitgeber. It was calculated as the ratio between the variance of the average 24-h pattern around the mean and the overall variance. Mathematically, it is expressed as follows: where n is the total number of data points, p is the number of data points per day, x h are the hourly means, x is the mean of all data, and x i represents the individual data points collected. The IV indicates the fragmentation of the rhythm, i.e., the frequency and extent of transitions between high and low values. It is calculated as the ratio of the mean square difference between successive hours (first derivative) and the mean square deviation from the grand mean (overall variance): We also considered L5 and L5 start-time as variables which represent the five consecutive hours with the lowest average glucose levels and its time of onset. M10 and M10 start-time were also calculated, these variables express the 10 consecutive hours with the highest average glucose levels and its time of onset. RA is calculated by dividing amplitude by the sum of M10 and L5 24 , 25 . Finally, to calculate the first and last postprandial glucose peaks and their timing for every participant we performed a multi-component cosinor analysis in Python using the package cosinorPy 26 . All of these chronobiological metrics obtained from fitting glucose values to a circadian rhythm were considered as potential predictors in the subsequent models. Statistical analysis Continuous variables were summarized using means and their standard deviations and/or medians and interquartile ranges (IQRs). Categorical variables were depicted using frequencies and percentages. Maternal outcomes were compared between two groups using either the t-test or the Mann-Whitney where appropriate. For maternal categorical outcomes, comparisons between the two groups were made using the chi-square test or the Fisher exact test where appropriate. Neonatal outcomes were compared using generalized estimating equations (GEE) models to account for the correlation between twins in the study. We constructed a receiver operating characteristic curve (ROC) to evaluate the diagnostic utility of each of the 11 chronobiological metrics for predicting preeclampsia incidence. We used the Youden index to choose thresholds for predicting preeclampsia incidence. To further assess performance, we calculated Brier scores. Considering that the sample size is relatively small, we added a bootstrap procedure in the analysis to obtain optimism-corrected performance metrics area under the curve (AUC), sensitivity, and specificity 27 , 28 . Chronobiological metrics were then included in a multiple logistic regression to assess whether a combination of predictors would better predict preeclampsia incidence. Maternal age was also included as a covariate since it is well-known to be associated with preeclampsia incidence. Given the small sample size and number of events, we restricted the final model to have a total of 3 predictors. All analyses were performed in R software version 4.4.0., except the multi-component cosinor that was performed in Python 3.10.9. RESULTS The data comprised 92 subjects who participated in the study between June 2020 and January 2022, with an interruption in recruitment due to the COVID-19 pandemic. In this study, we included 71 participants, as our calculations did not iterate for 21 patients due to insufficient days recorded to analyze the multiple components models. The assessed sample did not show differences with the original sample (Supplementary Table 1). Among the 71 patients 19 were Hispanic (26.8%), 31 non-hispanic black (43.7 %) and 9 were non-hispanic white (12.7 %) ( Table 1 ). In addition, 16 women were overweight (22.5%), 44 showed obesity (62%) and 11 were classified as normal weight (15.5%). Furthermore, 11 patients had chronic hypertension (15.5%), 19 reported gestational hypertension (26.8%), 20 patients reported preeclampsia (28.2%) and 11 preeclampsia with severe features (15.5%). View this table: View inline View popup Table 1. Maternal characteristics Chronobiological metrics to predict preeclampsia incidence All participants exhibited rhythmicity in their glucose oscillations over a 24-hour period (see sample metrics in Supplementary Table 2). Table 2 presents the predictors showing that L5 start-time and the average time between the first and last postprandial peaks had the best performance in predicting preeclampsia based on their AUC and their Brier scores. View this table: View inline View popup Download powerpoint Table 2. Glucose circadian rhythm variables and receiver operating characteristic curve for each variable when predicting adverse neonatal composite outcome. Logistic regression results We performed a logistic regression model including preeclampsia incidence as the dependent variable, while the time difference between the first and last postprandial glucose peaks and L5 start-time as predictors and controlling for age. This model ( Table 3 ) showed that higher separation than 12.6 hours between first postprandial glucose peak and the last one had a positive effect on preeclampsia incidence (OR: 4.37, 95% CI: 1.24-16.6, p < 0.05), while later times for L5 start-time (OR: 1.10, 95% CI: 1.01-1.21, p<0.05) and age (OR: 1.11, 95% CI: 1.02-1.22, p<0.05) contributed positively to preeclampsia incidence. When tested for preeclampsia incidence, this model showed an AUC of 0.800 (95% CI: 0.691-0.910), 88 % specificity, and 36 sensitivity. In addition, after correction for optimism with internal validation through bootstrapping, we got the same values with an AUC of 0.802 (95% CI: 0.693 - 0.912), 88% specificity, 37% sensitivity, and Brier score of 0.156 (see Supplementary Table 3 for optimism metrics details). View this table: View inline View popup Download powerpoint Table 3. Logistic regression models for preeclampsia incidence Furthermore, we studied differences between circadian variables between those predicted as preeclamptic pregnancies and non-preeclamptic by our mode (Supplementary Table 4). In this way, our model showed that those women predicted for preeclampsia had a reduced amplitude for glucose oscillation (p<0.001), but also a reduced robustness for rhythmicity (IS, p <0.05). Based on this new model to predict preeclampsia, we did not observe any difference in maternal characteristics depending on the prediction of preeclampsia, except for a higher proportion of subjects above 35 years old (p = 0.05) (Supplementary Table 5). We did not observe any significant maternal adverse outcome ( Table 4 ), but a higher proportion of preeclamptic patients was identified by our model (p = 0.07). When considering neonatal outcomes (Supplementary Table 6), our model showed that the group associated with preeclampsia had a reduced value for birth weight (p = 0.07). View this table: View inline View popup Download powerpoint Table 4: Maternal outcomes by our model to anticipate preeclampsia View this table: View inline View popup Download powerpoint Table 5. Internal validation of the model to predict preeclampsia. DISCUSSION Preeclampsia is considered to be one of the “great obstetrical syndromes” that causes 60,000 maternal deaths and more than 500,000 preterm births every year worldwide 29 . Classically, preeclampsia can be classified as early-onset preeclampsia and late-onset form 30 , 31 . The early-onset preeclampsia is defined as the form that develops before 34 weeks of gestation, and the late-onset form at or after 34 weeks of gestation. In the early onset form, preeclampsia has two stages: the first stage is a poor placentation, and the second stage is the maternal systemic disease. To date, there is growing evidence that pathophysiological functions involved in preeclampsia are normally regulated by the circadian system. In this way, multiple physiological functions following a 24-hour pattern appear dysregulated during preeclampsia 32 . CGM is a non-invasive method that is gaining popularity to monitor subcutaneous interstitial glucose at a high-resolution level in a non-invasive way 33 . In the last years, it has also been used to investigate pregnancy complications, mostly related to diabetes in pregnancy 18 , 34 . Currently, there is limited research using CGM focused on preeclampsia, and classical analyses like time in range, glucose variability, or mean differences have been computed to elucidate if CGM can be used to anticipate preeclampsia in T1DM due to their close association 35 – 37 . While Englund Ogge (2024) did not find any differences between preeclamptic women and non-preeclamptic women in patients with T1DM, Tiselko et al. (2022) reported a negative correlation between glucose variability and gestational age in T1DM with preeclampsia. Thus, these differences in glucose values to detect preeclampsia can only be applied to predict preeclampsia risk in T1DM. In our study, we included 71 pregnant individuals without diabetes to study whether a chronobiological approach could be used to predict preeclampsia. Studying the circadian pattern and even the timing of postprandial peaks showed differences in preeclamptic pregnancies compared to healthy pregnancies. We observed that individuals diagnosed later with preeclampsia had higher time differences between the first postprandial glucose peak and the last one, suggesting an extended eating schedule. Actually, those classified as preeclampsia gestations in our model showed more than a 12.6h difference between the first glucose postprandial peak and the last one. Experiments in rats have shown that time-restricted feeding (TRF) has been associated with reduced inflammation and pro-apoptotic markers found in preeclampsia 38 . Nighttime eating and later glucose peaks at night contribute to impaired glucose metabolism 39 . Actually, to control metabolic perturbations during gestation, TRF has been proposed as an effective way to align eating events with circadian physiology to optimize metabolism 40 . Studies suggesting that glucose peaks at night might have negative consequences on pregnancy outcomes have been focused on GDM 41 . Actually, obese women, a group with a higher risk of developing GDM and preeclampsia, among other pregnancy complications, have higher glucose peaks at night 42 . Despite the limited research between glucose circadian rhythms and the incidence of preeclampsia, multiple investigations support the relationship between impaired glucose metabolism, proper rhythmic physiology, and the development of this hypertensive disease. The early onset form of preeclampsia is associated with fetal growth restriction, and it comes from poor placentation due to abnormal trophoblast invasion into the uterus. This abnormal invasion can be triggered by high glucose concentrations 43 , 44 , provoking a shallow spiral artery remodeling during the first half of pregnancy 31 . This hyperglycemia linked to poor trophoblast migration has been linked to trophoblast inflammatory and antiangiogenic profile 45 . Recently, it has been reported that Bmal1 expression, a master circadian gene regulator, is needed to ensure trophoblast invasion, and when silenced, it represses trophoblast invasion via downregulation of metalloproteinases (TIMP3) 46 . Furthermore, regarding rhythmicity in metabolism, it has been observed that placenta in women with preeclampsia express less melatonin receptor (both MT1 and MT2), while melatonin has promoting invasion effects on trophoblasts 47 . Actually, MT1 receptor expression is maximal during the first trimester of pregnancy 48 , suggesting trophoblasts require a rhythmic metabolism but also a rhythmic environment for proper functioning. Another process involved in these initial stages in preeclampsia is endothelial dysfunction, which leads to trophoblast invasion disorder that can affect maternal spiral artery remodeling 44 . A phase I trial demonstrated that melatonin administration mitigated preeclampsia symptoms and improved endothelial function 49, reinforcing the idea that internal oscillating physiology and proper alignment with external zeitgebers might reduce preeclampsia incidence at the beginning of pregnancy 50 . During the second stage, clinical symptoms are observed and abnormalities in nitric oxide (NO) metabolism are caused by the endothelial dysfunction started in the previous stage 51 . Inhibition of NO metabolism increases platelet aggregation and inflammatory cell activation while impeding vasodilation 52 . These symptoms, if untreated, will progress to high blood pressure, stroke, systemic organ failure, cerebral edema or seizures 53 , 54 . It is known that melatonin from the maternal pineal gland increases during gestation in healthy pregnancies and shows lower levels in preeclamptic women. Actually, the deficiency in melatonin production during preeclamptic pregnancies is correlated with the severity of the disease 55 . It is well-known that melatonin synchronizes internal circadian rhythms and is a hypertensive agent, promoting NO synthesis in endothelium. Although we did not measure the rhythmicity of any of these outcomes in our investigation, we observed a decreased amplitude in glucose oscillation in preeclamptic women, suggesting an impaired internal oscillation in other physiological functions. Furthermore, circadian rhythms in the body are interconnected 56 , suggesting that the disruption observed in glucose rhythm could be present in other functions of the affected patients. Finally, our study faced some limitations, like the sample size and, consequently, the number of women affected by preeclampsia. Actually, our algorithm to predict preeclampsia had a good sensitivity, but its ability to predict false negatives (specificity) was low and potential results may be masked by false positives. In addition, the lack of external validation is a common problem when approaching new perspectives. Moreover, evaluating sleep or even assessing chronotype could provide valuable information to understand the extent to which glucose circadian rhythm and sleep are interconnected during pregnancy. Finally, despite the limitations of our study, this research opens the possibility to investigate whether the glucose circadian rhythm of other circadian physiological functions may predict the incidence of preeclampsia earlier than current strategies. DATA AVAILABILITY The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request pending application and approval by the research group. FUNDING No additional funding was required to perform this research COMPETING INTERESTS J.A.C. held stocks in Dexcom, Inc at study initiation; however, J.A.C. had relinquished all shares before conducting this research. The remaining authors report no conflict of interest. Study devices were provided by Dexcom, Inc. for the original research. No additional funding was provided. Dexcom, Inc had no role in the study design; conduction of the study; collection, analysis, and interpretation of data; or manuscript writing. REFERENCES 1. ↵ Deady , C. et al. An altered gut microbiome in pre-eclampsia: cause or consequence . Front. Cell. Infect. Microbiol . 14 , 1352267 ( 2024 ). OpenUrl PubMed 2. ↵ Staff , A. C. The two-stage placental model of preeclampsia: An update . J. Reprod. Immunol . 134–135 , 1 – 10 ( 2019 ). OpenUrl 3. ↵ Mitranovici , M.-I. et al. Understanding the Pathophysiology of Preeclampsia: Exploring the Role of Antiphospholipid Antibodies and Future Directions . J. Clin. Med. Res . 13 , ( 2024 ). 4. ↵ Xu , M. et al. Association Between Preeclampsia and Blood Pressure in Offspring: A Systematic Review and Meta-Analysis . Curr. Hypertens. Rep . ( 2024 ) doi: 10.1007/s11906-024-01306-3 . OpenUrl CrossRef 5. ↵ Vidaeff , A. C. , Saade , G. R. & Sibai , B. M. Preeclampsia: The Need for a Biological Definition and Diagnosis . Am. J. Perinatol . 38 , 976 – 982 ( 2021 ). OpenUrl PubMed 6. ↵ Wang , H. et al. Bioinformatics identification and validation of maternal blood biomarkers and immune cell infiltration in preeclampsia: An observational study . Medicine 103 , e38260 ( 2024 ). OpenUrl PubMed 7. ↵ Salzer , L. , Tenenbaum-Gavish , K. & Hod , M. Metabolic disorder of pregnancy (understanding pathophysiology of diabetes and preeclampsia) . Best Pract. Res. Clin. Obstet. Gynaecol . 29 , 328 – 338 ( 2015 ). OpenUrl CrossRef PubMed 8. ↵ Vidaeff , A. C. , Saade , G. R. & Sibai , B. M. Preeclampsia: The Need for a Biological Definition and Diagnosis . Am. J. Perinatol . 38 , 976 – 982 ( 2021 ). OpenUrl PubMed 9. ↵ Bravo , R. et al. High-fat diet and glucose and albumin circadian rhythms’ chronodisruption in rats . Turk. J. Biol . 41 , 364 – 369 ( 2017 ). OpenUrl 10. ↵ Nugteren , J. J. et al. Work-related maternal risk factors and the risk of pregnancy induced hypertension and preeclampsia during pregnancy. The Generation R Study . PLoS One 7 , e39263 ( 2012 ). OpenUrl CrossRef PubMed 11. ↵ Zhou , G. et al. Placental circadian lincRNAs and spontaneous preterm birth . Front. Genet . 13 , 1051396 ( 2022 ). OpenUrl PubMed 12. ↵ Larry , C. D. & Yeo , S. The circadian rhythm of blood pressure during pregnancy . J. Obstet. Gynecol. Neonatal Nurs . 29 , 500 – 508 ( 2000 ). OpenUrl PubMed 13. ↵ Gupta , H. P. et al. Circadian pattern of blood pressure in normal pregnancy and preeclampsia . J. Obstet. Gynaecol. India 61 , 413 – 417 ( 2011 ). OpenUrl PubMed 14. ↵ Hammer , P. et al. Night work and hypertensive disorders of pregnancy: a national register-based cohort study . Scand. J. Work Environ. Health 44 , 403 – 413 ( 2018 ). OpenUrl PubMed 15. Takmaz , T. et al. Are chronotype and subjective sleep quality associated with preeclampsia and preterm birth? Biol. Rhythm Res . 53 , 159 – 171 ( 2022 ). OpenUrl 16. ↵ Agarwal , I. et al. First-trimester occupational exposures and hypertensive disorders of pregnancy among US nurses . Occup. Med . 73 , 419 – 426 ( 2023 ). OpenUrl PubMed 17. ↵ Diallo , A. B. et al. Disruption of the Expression of the Placental Clock and Melatonin Genes in Preeclampsia . Int. J. Mol. Sci . 24 , ( 2023 ). 18. ↵ Fishel Bartal , M. et al. Continuous glucose monitoring in individuals undergoing gestational diabetes screening . Am. J. Obstet. Gynecol . 229 , 441 .e1-441.e14 ( 2023 ). OpenUrl 19. ↵ Duryea , E. L. , Hawkins , J. S. , McIntire , D. D. , Casey , B. M. & Leveno , K. J. A revised birth weight reference for the United States . Obstet. Gynecol . 124 , 16 – 22 ( 2014 ). OpenUrl PubMed 20. ↵ Bravo , R. et al. High-fat diet and glucose and albumin circadian rhythms’ chronodisruption in rats . Turk. J. Biol . 41 , 364 – 369 ( 2017 ). OpenUrl 21. ↵ Bravo Santos , R. et al. Activity/inactivity circadian rhythm shows high similarities between young obesity-induced rats and old rats . Physiol Int 103 , 65 – 74 ( 2016 ). OpenUrl PubMed 22. ↵ Zhang , B. & Sannajust , F. Diurnal rhythms of blood pressure, heart rate, and locomotor activity in adult and old male Wistar rats . Physiol. Behav . 70 , 375 – 380 ( 2000 ). OpenUrl CrossRef PubMed Web of Science 23. ↵ Blume , C. , Santhi , N. & Schabus , M. “nparACT” package for R: A free software tool for the non-parametric analysis of actigraphy data . MethodsX 3 , 430 – 435 ( 2016 ). OpenUrl PubMed 24. ↵ Bravo , R. et al. Body weight gain in rats by a high-fat diet produces chronodisruption in activity/inactivity circadian rhythm . Chronobiol. Int . 31 , 363 – 370 ( 2014 ). OpenUrl CrossRef PubMed 25. ↵ Gupta , P. et al. Circadian rest-activity misalignment in critically ill medical intensive care unit patients . J. Sleep Res . 31 , e13587 ( 2022 ). OpenUrl PubMed 26. ↵ Moškon , M. CosinorPy: a python package for cosinor-based rhythmometry . BMC Bioinformatics 21 , 485 ( 2020 ). OpenUrl PubMed 27. ↵ Steyerberg , E. W. et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis . J. Clin. Epidemiol . 54 , 774 – 781 ( 2001 ). OpenUrl CrossRef PubMed Web of Science 28. ↵ Steyerberg , E. W. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating . ( Springer , 2019 ). 29. ↵ Ma’ayeh , M. & Costantine , M. M. Prevention of preeclampsia . Semin. Fetal Neonatal Med . 25 , 101123 ( 2020 ). OpenUrl PubMed 30. ↵ Redman , C. W. Current topic: pre-eclampsia and the placenta . Placenta 12 , 301 – 308 ( 1991 ). OpenUrl CrossRef PubMed Web of Science 31. ↵ Roberts , J. M. & Hubel , C. A. The two stage model of preeclampsia: variations on the theme . Placenta 30 Suppl A , S32 – 7 ( 2009 ). OpenUrl CrossRef PubMed Web of Science 32. ↵ Zhou , G. et al. Co-alterations of circadian clock gene transcripts in human placenta in preeclampsia . Sci. Rep . 12 , 17856 ( 2022 ). OpenUrl CrossRef PubMed 33. ↵ Bravo , R. et al. Glucose circadian rhythm assessment in pregnant women for gestational diabetes screening . Int. J. Obes . ( 2024 ) doi: 10.1038/s41366-024-01636-x . OpenUrl CrossRef 34. ↵ Bitar , G. et al. Continuous Glucose Monitoring and Time in Range: Association with Adverse Outcomes among People with Type 2 or Gestational Diabetes Mellitus . Am. J. Perinatol . 41 , e1370 – e1377 ( 2024 ). OpenUrl PubMed 35. ↵ Jones , L. V. , Ray , A. , Moy , F. M. & Buckley , B. S. Techniques of monitoring blood glucose during pregnancy for women with pre-existing diabetes . Cochrane Database Syst. Rev . 5 , CD009613 ( 2019 ). OpenUrl PubMed 36. Tiselko , A. V. et al. Glucose variability as the risk factor of preeclampsia in pregnant patients with type 1 diabetes mellitus . J. Matern. Fetal. Neonatal Med . 35 , 9639 – 9646 ( 2022 ). OpenUrl PubMed 37. ↵ Englund Ögge , L. et al. Glycemic control assessed by continuous glucose monitoring during pregnancy in women with type 1 diabetes and its association with preeclampsia, an observational Swedish cohort study . Acta Obstet. Gynecol. Scand . 103 , 1426 – 1436 ( 2024 ). OpenUrl PubMed 38. ↵ Upadhyay , A. et al. Time-restricted feeding reduces high-fat diet associated placental inflammation and limits adverse effects on fetal organ development . Biochem. Biophys. Res. Commun . 514 , 415 – 421 ( 2019 ). OpenUrl PubMed 39. ↵ Phelps , R. L. , Metzger , B. E. & Freinkel , N. Carbohydrate metabolism in pregnancy . Am. J. Obstet. Gynecol . 140 , 730 – 736 ( 1981 ). OpenUrl CrossRef PubMed Web of Science 40. ↵ Flanagan , E. W. , Kebbe , M. , Sparks , J. R. & Redman , L. M. Assessment of Eating Behaviors and Perceptions of Time-Restricted Eating During Pregnancy . J. Nutr . 152 , 475 – 483 ( 2022 ). OpenUrl CrossRef PubMed 41. ↵ Tan , N. A. , Davidson , Z. E. , Grieger , J. A. & Bonham , M. P. Time of day and glycaemic response in pregnant women: A gap in current guidelines? Clin Nutr ESPEN 61 , 219 – 223 ( 2024 ). OpenUrl PubMed 42. ↵ Yogev , Y. et al. Diurnal glycemic profile in obese and normal weight nondiabetic pregnant women . Am. J. Obstet. Gynecol . 191 , 949 – 953 ( 2004 ). OpenUrl CrossRef PubMed Web of Science 43. ↵ Belkacemi , L. , Lash , G. E. , Macdonald-Goodfellow , S. K. , Caldwell , J. D. & Graham , C. H. Inhibition of human trophoblast invasiveness by high glucose concentrations . J. Clin. Endocrinol. Metab . 90 , 4846 – 4851 ( 2005 ). OpenUrl CrossRef PubMed 44. ↵ Tao , J. et al. High glucose condition inhibits trophoblast proliferation, migration and invasion by downregulating placental growth factor expression . J. Obstet. Gynaecol. Res . 46 , 1690 – 1701 ( 2020 ). OpenUrl PubMed 45. ↵ Heim , K. R. et al. Excess glucose induce trophoblast inflammation and limit cell migration through HMGB1 activation of Toll-Like receptor 4 . Am. J. Reprod. Immunol . 80 , e13044 ( 2018 ). OpenUrl PubMed 46. ↵ Lv , S. et al. Impaired decidualization caused by downregulation of circadian clock gene BMAL1 contributes to human recurrent miscarriage . Biol. Reprod . 101 , 138 – 147 ( 2019 ). OpenUrl PubMed 47. ↵ Liu , Z. , Chen , B. , Chang , J. , Feng , L. & Zhao , X. Melatonin regulates trophoblast pyroptosis, invasion and migration in preeclampsia by inhibiting HtrA1 transcription through the microRNA-520c-3p/SETD7 axis . Am. J. Reprod. Immunol . 87 , e13523 ( 2022 ). OpenUrl PubMed 48. ↵ Soliman , A. et al. Placental melatonin system is present throughout pregnancy and regulates villous trophoblast differentiation . J. Pineal Res . 59 , 38 – 46 ( 2015 ). OpenUrl CrossRef PubMed 49. Hobson , S. R. et al. Melatonin improves endothelial function in vitro and prolongs pregnancy in women with early-onset preeclampsia . J. Pineal Res . 65 , e12508 ( 2018 ). OpenUrl CrossRef PubMed 50. ↵ Ditisheim , A. J. , Dibner , C. , Philippe , J. & Pechère-Bertschi , A. Biological rhythms and preeclampsia . Front. Endocrinol . 4 , 47 ( 2013 ). OpenUrl 51. ↵ Ornaghi , S. & Paidas , M. J. Novel Therapy for the Treatment of Early-Onset Preeclampsia . Clin. Obstet. Gynecol . 60 , 169 – 182 ( 2017 ). OpenUrl PubMed 52. ↵ Zullino , S. , Buzzella , F. & Simoncini , T. Nitric oxide and the biology of pregnancy . Vascul. Pharmacol . 110 , 71 – 74 ( 2018 ). OpenUrl PubMed 53. ↵ Bokslag , A. , van Weissenbruch , M. , Mol , B. W. & de Groot , C. J. M. Preeclampsia; short and long-term consequences for mother and neonate . Early Hum. Dev . 102 , 47 – 50 ( 2016 ). OpenUrl CrossRef PubMed 54. ↵ Narkhede , A. M. & Karnad , D. R. Preeclampsia and Related Problems . Indian J. Crit. Care Med . 25 , S261 – S266 ( 2021 ). OpenUrl PubMed 55. ↵ Langston-Cox , A. , Marshall , S. A. , Lu , D. , Palmer , K. R. & Wallace , E. M. Melatonin for the Management of Preeclampsia: A Review . Antioxidants (Basel) 10 , ( 2021 ). 56. ↵ Woller , A. & Gonze , D. Circadian Misalignment and Metabolic Disorders: A Story of Twisted Clocks . Biology 10 , ( 2021 ). View the discussion thread. Back to top Previous Next Posted February 06, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Glucose Circadian Rhythm Disruption is Associated with Preeclampsia Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Glucose Circadian Rhythm Disruption is Associated with Preeclampsia R Bravo , KH Lee , SA Nazeer , J Ashby Cornthwaite , B Sibai , M Fishel Bartal , C Pedroza medRxiv 2025.02.05.25321670; doi: https://doi.org/10.1101/2025.02.05.25321670 Share This Article: Copy Citation Tools Glucose Circadian Rhythm Disruption is Associated with Preeclampsia R Bravo , KH Lee , SA Nazeer , J Ashby Cornthwaite , B Sibai , M Fishel Bartal , C Pedroza medRxiv 2025.02.05.25321670; doi: https://doi.org/10.1101/2025.02.05.25321670 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Sexual and Reproductive Health Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff525a869898e2e',t:'MTc3OTM4MjkyOA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();