disruption of circadian rhythms of preterm pa and pprom · disruption of circadian rhythms of...

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Disruption of Circadian Rhythms of preterm PAand PPROM

Department of EpidemiologyMiguel Angel Luque-Fernandez

Ananth C.V., Sanchez S.E., Qui Chun-fang, Hernandez-Diaz S., Unnur ValdimarsdottirBizu Gelaye, Michelle A. Williams

June 24, 2014

HSPH-Department of Epidemiology Disruption of Circadian Rhythms of preterm PA and PPROM

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Outline1 Background

IntroductionThe hypothesisJustificationObjectives

2 MethodsData, settings and sample sizeStatistical analysis: Circular Time and Fourier Transformation

3 ResultsResultsReplication: NCPP study

4 DiscussionMain findingsLimitationsInterpretationConclusions

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Background

Globally, 15 million preterm deliveries are estimated to take placeevery year.

Around 1 million children die each year due to complications ofprematurity and placental abruption (PA).

PA is a significant contributor to maternal mortality and it occurs in1% of pregnancies worldwide.

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Background

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Background

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Background

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The hypothesis

The time of onset of preterm PA and extremely and very PPROMcases may be characterized by a disrupted circadian patternassociated with fetal immaturity.

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The justification

Justification

The disruption of circadian rhythms driven by fetal immaturity mayhelp to explain tocolytics failure to prevent delivery of veryand extremely PPROM cases, and therefore could help toorientate the clinical practice.

The time of the onset of PPROM and PA cases has not beenmodeled with the most appropriate methodology . Therefore, anappropriate methodological approach could help to test moreconsistently the hypothesis.

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The justification

Justification

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The justification

Justification

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Rabiddran R. et al. BJOG 2010;117:1656-1657

𝒙𝟐

Lindow S.W. et al. British Journal of Obstetrics and Gynecology 2000;107:1145-1148

Time plot

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The objectives

Objectives

1 To model, with the appropriate methodology (time series theory andcircular time), the time of onset of PPROM and PA cases.

2 To evaluate the presence of circadian rhythms for PPROM and PAcases.

3 To examine the extent to which the circadian rhythms vary based ongestational age at onset.

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The objectives

Objectives

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The objectives

Objectives

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The objectives

Objectives

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Data, settings and sample sizeStatistical analysis: Circular Time and Fourier Transformation

METHODS

Data, setting and sample size.

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Data, settings and sample size

Data, setting and sample size

A total of 232 PPROM and 163 PA singleton spontaneous caseswere identified at three hospitals in Lima, Peru, between January2009 and July 2010.

Exclusions: Multiplicity, labor induction and/or artificial rupture ofmembranes and gestational age unknown.

Gestational age: based on the date of the LMP and confirm by anultrasound before 20 weeks of gestation.

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Methods: Case definition

Study Case definition

A certified nurse midwife abstracted detailed information includingthe time of onset of PPROM and PA cases from maternal medicalrecords.

The time of onset of spontaneous PPROM was defined as womenself-reported starting time of leaking or gushing of fluid from thevagina, and confirmed by medical exploration at the time of theadmission.

The time of the onset of PA was defined as women’s self-reportedstarting of profuse external bleeding accompanied by uterinetenderness or hypertonia plus intense back pain, confirmed bymedical anamnesis at the moment of the admission.

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METHODS

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Methods

Time in a linear scale

The truth is never linear!Or almost never!

But often the linearity assumption is good enough.When its not:

Time in circular scale

Circular time: trigonometric models or cosinor models

Polynomials, trigonometric polynomials, fractional polynomials

Splines, restricted cubic splines, smoothed splines...

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Methods

The truth is never linear!Or almost never!But often the linearity assumption is good enough.

When its not:

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Methods

The truth is never linear!Or almost never!But often the linearity assumption is good enough.When its not:

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Methods

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Methods general overview

Circular Time

-(1/2)

(1/2)y

-1 1Circumference of Radious 1

Circular time

Assessing periodicity : FourierTimes Series Transformation(Periodogram).

Describing periodicity : Datareduction.Modeling periodicity :Trigonometric predictors with sineand cosine terms (Trigonometricregression or cosinor model).

Circular time modeling assumptions�� Sinusoidal pattern ————————�� Stationary time series

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Circular Time

-(1/2)

(1/2)y

Circular time

Assessing periodicity : FourierTimes Series Transformation(Periodogram).Describing periodicity : Datareduction.

Modeling periodicity :Trigonometric predictors with sineand cosine terms (Trigonometricregression or cosinor model).

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ResultsDiscussion

Circular Time

-(1/2)

(1/2)y

Circular time

Assessing periodicity : FourierTimes Series Transformation(Periodogram).Describing periodicity : Datareduction.Modeling periodicity :Trigonometric predictors with sineand cosine terms (Trigonometricregression or cosinor model).

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Circular Time

-(1/2)

(1/2)y

Circular time

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Circular Time

-(1/2)

(1/2)y

Circular time

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ResultsReplication: NCPP study

RESULTS

RESULTS AND COMMENTS ONMETHODS

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Results: Describing/assessing periodicity

Describing the data: Data reduction

Aggregated number of cases and percentages by hours and the foursegments of the day.

Prevalence ratios (PRs) of the aggregated number of cases bysegments of the day with night as reference(PRs were derived using a generalized linear Poisson model with linklog and scaled error standards to account for over-dispersion).

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Results: assessing periodicity

Kernel smoother and Fourier Transformation

1 We used a non-parametric Kernel density estimation to estimate theprobability density function of PPROM and PA cases over time (24hours).

2 We used a periodogram (Fourier transformation: sine and cosinefunctions) to identify any periodicity: higher frequencies in thedistribution of the observed data.

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Assessing periodicity: distributions and Periodogram

MA Luque-Fernandez et al., non published data. Under review

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Results: TESTING PERIODICITY

Testing periodicity: Cosinor models

Modeling the time of onset of PPROM AND PA: Cosinor models

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The cosinor Model: trigonometric regression

Cosinor

The Cosinor model: Generalized Linear Model

g(Y )t = c cos(wt) + s sin(wt)

t=1,...,24.Scaling time to a circular scale.

We compute wt as follow:

wt = 2π ft

ft =timet − 0.5

24hours

g(.) = Onsets counts; family: Poisson andscaled S.E (φ=

√χ2/df )

Deriving the amplitude and testing thestatistical significance of the circadian rhythm

Where the Amplitude is derived as follows:

A =√

c2 + s2 , (A ≥ 0)

Then we test the following Null HypothesisThe amplitude is A= 0

Using the following test:Z=nA2

Where:A =

√c2 + s2

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Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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ResultsDiscussion

Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Then we test the following Null Hypothesis

The amplitude is A= 0Using the following test:

Where:A =

√c2 + s2

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Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Using the following test:

Where:A =

√c2 + s2

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ResultsDiscussion

Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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ResultsDiscussion

Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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ResultsDiscussion

Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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ResultsDiscussion

Cosinor

wt = 2π ft

ft =timet − 0.5

24hours

√χ2/df )

A =√

c2 + s2 , (A ≥ 0)

Where:A =

√c2 + s2

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Amplitude

One cycle per 2π units of time

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Preterm Premature Rupture of Membranes in Lima, Peru, n= 232

c©MA Luque-Fernandez et al., non published data. Under review

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Preterm Premature Rupture of Membranes in Lima, Peru, n= 232

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Placental Abruption cases, in Lima, Peru, n= 163

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Placental Abruption cases, in Lima, Peru, n= 163

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NCPP REPLICATION

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Description of the NCCP study

The US National Collaborative Perinatal Project (NCPP)

This study was a prospective cohort study of 60,000 pregnantwomen between 1959-1966.

The NCPP was specifically designed to identify determinants ofcerebral palsy and other neurological defects.

We applied the same restriction as we did with the Peruvian data toobtain (2,262 PPROM and 374 PA cases).

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PA ≥37 gestation weeks

NCCP TERM PA cases

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TERM Placental Abruption NCPP, n= 231

Term spontaneous, singleton PA cases, (N=231, >=37 gestation weeks)

Circadian Test p-value <0.050

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time in 24 hours

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DISCUSSION

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Main findingsLimitationsInterpretationConclusions

Main findings

Disruption of Circadian Rhythms

We found a significant diurnal circadian pattern in the timing of theonset of PPROM cases and some evidence among term PA cases inLima, Peru.

We did not find evidence of a circadian pattern among cases of veryand extremely PPROM and PA.

NCPP replication analysis confirm our main findings.

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Main findings

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Main findings

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Main findings

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LIMITATIONS

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Limitations

Measurement error due to maternal self-reports starting signs andsymptoms.

High specificity for severe cases of PPROM and PA thoughcompromise in sensitivity for mild PA and PPROM cases.

The circadian test is dependent on the sample size.

Need of more replication studies in other geographic regions andpopulations.

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Limitations

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Limitations

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Limitations

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Limitations

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INTERPRETATION

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Interpretation

Our findings are consistent with previous evidence and may beexplained by a biological mechanism (HPA-axis).

The fetal immaturity of the HPA-axis may account for the absenceof circadian rhythms in extremely preterm deliveries.

Other mechanisms driven by infection (chorioamnionitis) andinflammation may explain the triggering of parturition in extremelyand very preterm cases.

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Interpretation

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Interpretation

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Interpretation

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CONCLUSIONS

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Conclusions

The time of onset of very PPROM and preterm PA cases ischaracterized by a disrupted circadian pattern.

This disrupted circadian pattern underlies other pathologicalmechanisms associated with the time of onset of PPROM and PAcase rather than a normal biological circadian rhythm.

The disrupted circadian pattern driven by fetal immaturity may helpto explain tocolytic failure to prevent delivery of very and extremelyPPROM cases.

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Conclusions

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Conclusions

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Conclusions

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References

Some important references

1 A. G. Barnett, A. J. Dobson, E. Library., Analyzing seasonal health data,Springer, 2010.

2 N. I. Fisher, Statistical analysis of circular data, Cambridge University Press,Cambridge, 1993.

3 P. Bloomfield, Fourier analysis of time series: an introduction, 2nd Edition,Wiley series in probability and statistics Applied probability and statistics,Wiley, New York ; Chichester, 2000.

4 T. W. Korner, Fourier analysis, Cambridge University Press, Cambridge, 1988

5 J. W. Hardin, J. Hilbe, Generalized linear models and extensions, 3rdEdition, Stata Press, College Station, Tex., 2012.

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Acknowledgments

Professor Michelle A. Williams. Chair of the EpidemiologyDepartment, Harvard School of Public Health.

Dr.Bizu Gelaye, Director of the MIRT program, EpidemiologyDepartment, Harvard School of Public Health.

Rose Traveling Fellowship Program in Chronic Disease Epidemiologyand Biostatistics.

Dr. Unnur Valdimoarsdottir, Center of Public Health Sciences,Faculty of Medicine, University of Iceland, Reykjavik, Iceland.

Dr. Sixto Sanchez, AC. PROESA and the Instituto NacionalMaterno Perinatal, Lima, Peru.

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Thank You

[email protected]

Alhambra: Granada, Spain

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Methods: Case definitions ICD-10

Case definition based on

ICD-10 O42 (PPROM) and O45 (PA).

ICD-10-O42 (PPROM): Spontaneous tearing of the membranessurrounding the fetus any time before the onset of obstetric laborprior 37 gestation weeks. Rupture of membranes will show poolingof fluid in the vagina or leakage of fluid from the cervix.

ICD-10-O45 (PA): Premature separation of the normally implantedplacenta from the uterus. Signs of varying degree of severity includeuterine bleeding, uterine muscle hypertonia, and fetal distress orfetal death.

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Preterm PROM Placental AbruptionTime in 24h n % n %01h:00’ 10 4.3 5 3.102h:00’ 6 2.6 14 8.603h:00’ 11 4.7 4 2.504h:00’ 0 0.0 1 0.605h:00’ 15 6.5 5 3.106h:00’ 26 11.2 12 7.407h:00’ 14 6.0 11 6.808h:00’ 17 7.3 13 8.009h:00’ 9 3.9 10 6.110h:00’ 27 11.6 5 3.111h:00’ 7 3.0 7 4.312h:00’ 8 3.5 6 3.713h:00’ 4 1.7 5 3.114h:00’ 7 3.0 4 2.515h:00’ 5 2.2 10 6.116h:00’ 8 3.5 5 3.117h:00’ 8 3.5 3 1.818h:00’ 9 3.9 4 2.519h:00’ 2 0.9 9 5.520h:00’ 13 5.6 6 3.721h:00’ 5 2.2 8 4.922h:00’ 12 5.2 8 4.923h:00’ 6 2.6 5 3.124h:00’ 3 1.3 3 1.8

Table: Counts of preterm PROM and PA onsets by hour per day, in Lima, Peru,2009-2010 (Preterm PROM, n=232 and Placental Abruption, n= 163)

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Preterm PROM Placental AbruptionTime in 24h n % n %01h:00’ 10 4.3 5 3.102h:00’ 6 2.6 14 8.603h:00’ 11 4.7 4 2.504h:00’ 0 0.0 1 0.605h:00’ 15 6.5 5 3.106h:00’ 26 11.2 12 7.407h:00’ 14 6.0 11 6.808h:00’ 17 7.3 13 8.009h:00’ 9 3.9 10 6.110h:00’ 27 11.6 5 3.111h:00’ 7 3.0 7 4.312h:00’ 8 3.5 6 3.713h:00’ 4 1.7 5 3.114h:00’ 7 3.0 4 2.515h:00’ 5 2.2 10 6.116h:00’ 8 3.5 5 3.117h:00’ 8 3.5 3 1.818h:00’ 9 3.9 4 2.519h:00’ 2 0.9 9 5.520h:00’ 13 5.6 6 3.721h:00’ 5 2.2 8 4.922h:00’ 12 5.2 8 4.923h:00’ 6 2.6 5 3.124h:00’ 3 1.3 3 1.8

Table: Counts of preterm PROM and PA onsets by hour per day, in Lima, Peru,2009-2010 (Preterm PROM, n=232 and Placental Abruption, n= 163)

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PPROM onsets

Distribution of preterm PROM onsets by segment of the day and gestation weeks, in Lima,Peru, 2009-2010 (Preterm PROM, n=232)

Preterm Premature Rupture of Membranes (n= 232)(≥32-<37 weeks, n=154) (<32 weeks, n=78)

Segments of the day n(%) PR*(95%CI) n(%) PR(95%CI)Night01h:00’ to 06h:00’ 50(32.5) 1.64(1.39, 1.94) 18(23.1) 1.45(1.16, 1.83)Morning07h:00’ to 12:00’ 56(36.4) 1.67(1.45, 1.92) 26(33.3) 1.47(1.24, 1.75)Afternoon13h:00’ to 18h:00’ 25(16.2) 0.76(0.67, 0.88) 16(20.5) 0.74(0.65, 0.85)Evening/night19h:00’ to 24h:00’ 23(14.9) Ref. 18(23.1) Ref.* PR: Prevalence Ratio

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PPROM onsets

Distribution of preterm PROM onsets by segment of the day and gestation weeks, in Lima,Peru, 2009-2010 (Preterm PROM, n=232)

Preterm Premature Rupture of Membranes (n= 232)(≥32-<37 weeks, n=154) (<32 weeks, n=78)

Segments of the day n(%) PR*(95%CI) n(%) PR(95%CI)Night01h:00’ to 06h:00’ 50(32.5) 1.64(1.39, 1.94) 18(23.1) 1.45(1.16, 1.83)Morning07h:00’ to 12:00’ 56(36.4) 1.67(1.45, 1.92) 26(33.3) 1.47(1.24, 1.75)Afternoon13h:00’ to 18h:00’ 25(16.2) 0.76(0.67, 0.88) 16(20.5) 0.74(0.65, 0.85)Evening/night19h:00’ to 24h:00’ 23(14.9) Ref. 18(23.1) Ref.* PR: Prevalence Ratio

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Assessing Periodicity: Periodogram

Number of cycles in 2π time

The periodogram I(wj) is always positive,and it will be larger at frequencies thatare strongly represented in the data.Therefore the number of time pointsneeded to complete a cycle of 2π couldbe computed as the inverse of theFourier frequency using:

1/fj =2π

Formulae

I (wj) =2

n(C 2 + S2) j = 1, ...n/2

C 2 = 2n∑

yt cos(wj t)/n,

S2 = 2n∑

yt sin(wj t)/n,

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Assessing Periodicity: Periodogram

Number of cycles in 2π time

The periodogram I(wj) is always positive,and it will be larger at frequencies thatare strongly represented in the data.Therefore the number of time pointsneeded to complete a cycle of 2π couldbe computed as the inverse of theFourier frequency using:

1/fj =2π

Formulae

I (wj) =2

n(C 2 + S2) j = 1, ...n/2

C 2 = 2n∑

yt cos(wj t)/n,

S2 = 2n∑

yt sin(wj t)/n,

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Example

Periodogram of 25OHD serum concentrations and highest frequency

0.083 Highest frequency: Annual cycle (12 months)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

Frequency

Evaluated at the natural frequencies

c©MA Luque-Fernandez et al.Seasonal Variation of 25-Hydroxyvitamin D among non-Hispanic Black and White PregnantWomen from Three US Pregnancy Cohorts. Pediatrics and Perinatal Epidemiology 2013

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Sine and Cosine

Sine and Cosine functions

The steady rise-and-fall of the cosine and sine functions makes themideal for modeling seasonality or circadian patterns.

This rise-and-fall pattern is repeat.

This repeating property of the sine and cosine functions means thatwe only need to consider times from 0 to ≤ 2π.

Circular Time

The value of 2π is a key constant because it is the circumference of circlewith radius 1.

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Sine and Cosine

Circular Time

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Sine and Cosine

Circular Time

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Sine and Cosine

Circular Time

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Sine and Cosine

Circular Time

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Sine and Cosine Functions

One cycle per 2π units of time Two cycles per 4π units of time

0 p/2 p 3p/2 2p

TIME (x)

cosine

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Sine and Cosine Functions

One cycle per 2π units of time Two cycles per 4π units of time

0 p/2 p 3p/2 2p

TIME (x)

cosine

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We used a non-parametric EPANECHNIKOV Kernel densityestimation to estimate the probability density function of PPROMand PA cases over time (24 hours).

We used an OPTIMAL smoothing parameter because its densityestimate is close to the true density.

Fourier transformation (Periodogram).Transformation of time in a wave of sine and cosine functions inorder to identify the higher frequency of the observed data, andtherefore to identify a periodicity.

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Fourier transformation (Periodogram).

Transformation of time in a wave of sine and cosine functions inorder to identify the higher frequency of the observed data, andtherefore to identify a periodicity.

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Sensitivity analysis: cosinor vs. the best good need of fit.

Restricted Cubic spline function with K knots

f (x) =3∑

β0jxj +

k∑k=1

βi (x − tk)3

yi = f (xi ) + εi

Different number of Knots were placed at regularly spaced intervals (two hours, four and sixhours).

Polynomial trigonometric regression

Yi = β0 + β1 sin(wt) + β2 cos(wt) + β3 sin2(wt) + β4 cos

2(wt) + (...) + βdxd + εi

The number of trigonometric terms in the polynomial regression was selected using a k-foldcross validation strategy.

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Sensitivity analysis: cosinor vs. the best good need of fit.

Restricted Cubic spline function with K knots

f (x) =3∑

β0jxj +

k∑k=1

βi (x − tk)3

yi = f (xi ) + εi

Different number of Knots were placed at regularly spaced intervals (two hours, four and sixhours).

Polynomial trigonometric regression

Yi = β0 + β1 sin(wt) + β2 cos(wt) + β3 sin2(wt) + β4 cos

2(wt) + (...) + βdxd + εi

The number of trigonometric terms in the polynomial regression was selected using a k-foldcross validation strategy.

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Moderate PPROM 32 to 36 gestation weeks

NCCP MODERATE PPROM CASES (32 to 36 gestation weeks)

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Moderate Preterm Premature Rupture of Membranes NCPP, n= 1736

NCPP spontaneous singleton PPROM cases, (N=1736, 32 to 36 gestation weeks)

Circadian Test p-value <0.0140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time in 24 hours

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PPROM <32 gestation weeks

NCCP EXTREMELY AND VERY PPROM CASES (<32 gestationweeks)

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Extremely and Very Preterm Premature Rupture of Membranes NCPP, n= 526

Circadian Test p-value >0.05

NCPP PPROM spontaneous singleton cases,(N= 526, <32 gestation weeks)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time in 24 hours

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MODERATE PRETERM PA 32 to 36 gestation weeks

NCCP MODERATE PRETERM PAcases

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Moderate Preterm Placental Abruption NCPP, n= 83

NCPP Preterm spontaneous singleton PA cases, (N=83, 32 to 36 gestation weeks)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time in 24 hours

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PA <32 gestation weeks

NCCP EXTREMELY AND VERYPRETERM PA cases

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Extremely and Very Placental Abruption cases, NCPP, n= 60

NCPP spontaneous singleton PA cases, (N=60, <32 gestation weeks)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time in 24 hours

disruption of circadian rhythms of preterm pa and pprom · disruption of circadian rhythms of...

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