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\begin{document}

\title{Novel HumanMethylatyion450 analyses reveal DNA methylation predictors of
future diabetes onset in women with Polycystic Ovarian Syndrome}



\author[1]{MARK kitchen}%
\author[1]{Ayesha Manzur}%
\author[1]{Anthony Fryer}%
\author[1]{Pensee Wu}%
\affil[1]{Keele University}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Aims. Polycystic ovarian syndrome (PCOS) is a common endocrine disorder
characterised by ovarian morphological, systemic biochemical, and
menstrual changes. Women with PCOS are at significantly increased risk
of raised fasting glucose, impaired glucose tolerance, and diabetes.
Recognition of these complications and early intervention are key to
good health outcomes. We sought to identify DNA methylation patterns
that may predict future diabetes onset in this high-risk PCOS
population. Patients and Methods. Peripheral blood samples from women
with PCOS and women with PCOS who later developed diabetes, were
analysed by Illumina HumanMethylation450 BeadChip-arrays.
Bisulphite-Pyrosequencing was used to validate and confirm array
methylation data. Results. Array analyses identified 273 differentially
methylated CpG loci ([?]0.2 \selectlanguage{greek}β-\selectlanguage{english}value change) at initial diagnosis of PCOS,
between women who did or did not later develop diabetes. 19 of these
sites demonstrated differential methylation in the same direction in
[?]five sample pairs. Methylation in three of the candidates (cg11897887,
cg02819655, and cg25542007) showed the best concordance with
corresponding array \selectlanguage{greek}β-\selectlanguage{english}values, and, most clearly differentiated `cases'
from `controls'. Conclusions. We have identified novel methylation
biomarkers that could predict future onset of diabetes in this high-risk
population. Use of methylation analyses to identify women who are likely
to develop diabetes at diagnosis of PCOS may facilitate timely lifestyle
interventions to reduce future morbidity.%
\end{abstract}%



\sloppy


\textbf{Novel HumanMethylatyion450 analyses reveal DNA methylation
predictors of future diabetes onset in women with Polycystic Ovarian
Syndrome}

Mark O Kitchen (ORCID 0000-0002-1124-2940)\textsuperscript{1,2}, Ayesha
Manzur\textsuperscript{1}, Anthony A Fryer\textsuperscript{1}(ORCID
0000-0001-8678-0404), Pensee Wu (ORCID
0000-0003-0011-5636)*\textsuperscript{1,3}

(1) School of Medicine, Keele University, UK.

(2) Urology Department, University Hospitals of North Midlands NHS
Trust, UK.

(3) Academic Unit of Obstetrics and Gynaecology, University Hospitals of
North Midlands NHS Trust, UK

* Corresponding author: p.wu@keele.ac.uk

\textbf{Running Title:}

\textbf{DNA methylation predictors of diabetes in PCOS}

\emph{}

\emph{Abstract:}

\emph{* Objective}

To identify DNA methylation biomarkers at initial diagnosis of
Polycystic Ovarian Syndrome (PCOS) that could be used in conjunction
with patient factors (ethnicity, body mass index, family history) to
identify which women are likely to develop diabetes, and guide early
lifestyle intervention.

\emph{* Design}

Retrospective cohort study.

\emph{* Setting}

Department of Maternal and Fetal Medicine, University Hospitals of North
Midlands NHS Trust, UK.

\emph{* Population or Sample}

Women newly diagnosed with PCOS and women with PCOS that later developed
diabetes.

\emph{* Methods}

Peripheral blood samples from women with PCOS and women with PCOS who
later developed diabetes (taken at initial diagnosis of PCOS), were
analysed by Illumina HumanMethylation450 BeadChip-arrays.
Bisulphite-Pyrosequencing was used to validate and confirm array
methylation data.

\emph{* Main Outcome Measures}

Identification of differential methylation at diagnosis in women who did
or did not later develop diabetes.

\emph{* Results}

Array analyses identified 273 differentially methylated CpG loci ([?]0.2
\selectlanguage{greek}β-\selectlanguage{english}value change) at initial diagnosis of PCOS, between women who did or
did not later develop diabetes. 19 of these sites demonstrated
differential methylation in the same direction in [?]five sample pairs.
Methylation in three of the candidates (cg11897887, cg02819655, and
cg25542007) showed the best concordance with corresponding array
\selectlanguage{greek}β-\selectlanguage{english}values, and, most clearly differentiated `cases' from `controls'.

\emph{* Conclusions}

We have identified novel methylation biomarkers that could predict
future onset of diabetes in this high-risk population. Use of
methylation analyses to identify women who are likely to develop
diabetes at diagnosis of PCOS may facilitate timely lifestyle
interventions and reduce future morbidity.

\emph{* Tweetable abstract}

DNA methylation patterns might predict which women with PCOS will
develop diabetes

\emph{* Funding}

University Hospitals of North Midlands NHS Trust Charitable Fund

\emph{}

\emph{Introduction}

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder that
affects 6-15\% of women worldwide, and prevalence is steadily
increasing{[}1,2{]}. It is characterised by oligomenorrhea and/or olig-
or an-ovulation, androgen excess, and polycystic ovaries (PCO),
manifesting as menstrual irregularities, subfertility, hirsutism, acne,
metabolic syndrome, and reduced quality of life{[}1,2{]}.

PCOS is associated with impaired fasting glucose (IFG), impaired glucose
tolerance (IGT), and diabetes, all of which are risk factors for
cardiovascular morbidity and mortality, and chronic renal
disease{[}3{]}. Diabetes is approximately six-times more prevalent in
women with PCOS than aged-matched non-PCOS populations, and is the
greatest cause of systemic and chronic morbidity in these
women{[}3,4{]}. As such, primary prevention of diabetes is key in women
with PCOS.

Raised body-mass index (BMI), family history, and ethnicity are
risk-factors for developing diabetes in women with PCOS{[}4,5{]}.
However, individual risk-stratification is challenging and many women
receive sub-optimal lifestyle (e.g. diet, exercise) advice and delayed
intervention, with adverse outcomes{[}1,5{]}. Metabolomic and molecular
markers have thus far failed to reliably predict development of IGF, IGT
or diabetes in women with PCOS{[}6{]}. Differential DNA methylation
patterns have been found between healthy women and women with PCOS, and
between PCOS clinical phenotypes{[}7{]}. DNA methylation has also been
identified as holding prognostic value in multiple tissue types,
including predicting diabetes onset in non-PCOS populations{[}8{]}.

However, to our knowledge, there are no reports evaluating the
prognostic potential of differential methylation in women with PCOS in
relation to their risk of developing diabetes. We therefore performed
genome-wide (`450K' array) DNA methylation assessment comparing women
with PCOS and women with PCOS who later developed diabetes, to determine
whether novel potentially clinically useful `at diagnosis' biomarkers
that might guide early intervention and prevent development of diabetes
and associated complications, could be identified.

\emph{}

\emph{Patients and methods}

\emph{Human blood samples.}

Peripheral blood samples were obtained from women with PCOS diagnosed
\textgreater{}12 months prior to sample collection using Rotterdam
diagnostic criteria{[}1{]} (West Midlands (Black Country) Research
Ethics Committee 15/WM/0162). Repository samples with long-term clinical
data enabled identification and comparison of six `control' (women with
PCOS), and six `case' women with PCOS who developed diabetes (median
three years) following sample collection (demographics in
\textbf{TableS1} ). Samples were matched by ethnicity, and where
possible by age, BMI, smoking status, and co-morbidity. No patient had
prior history of endocrine/metabolic disorder, malignancy or systemic
chemotherapy.

\emph{DNA extraction and bisulphite modification.}

Genomic DNA was isolated using AllPrep DNA/RNA Mini kits (Qiagen) then
bisulphite-converted using EZ DNA Methylation Gold kits (Zymo
Research), according to the manufacturers' instructions.

\emph{Illumina Methylation Bead-Array Analyses.}

Bisulphite-converted DNA from each sample was hybridised to
HumanMethylation450 BeadChip arrays (Illumina, San Diego, USA), and
processed by Bart's and the London Genome Centre, UK. Array data were
processed by GenomeStudio software and the Novel Identification of
Methylation Biomarker Lists (NIMBL) platform{[}9{]}. NIMBL also assessed
internal control data, and performed peak-based correction to compensate
for probe-type sensitivity. CpGs where: (i) probe detection p-values
were \textgreater{}0.05, (ii) with missing \selectlanguage{greek}β-\selectlanguage{english}values, or (iii) known to
be associated with single nucleotide polymorphisms (SNPs), were
excluded.

\emph{Pyrosequencing of sodium bisulphite-converted DNA.}

Validation of array-identified methylation in all samples was performed
by Pyrosequencing of bisulphite-converted DNA, using PyroMark Q24
Pyrosequencer, PyroMark Q24 Software 2.0 and PyroMark Gold Q24 reagents,
according to the manufacturer's instructions.\emph{}

\emph{Results}

Initial array data screening identified 273 CpG loci where [?]5 of the six
matched PCOS `control' and the PCOS with subsequent-onset diabetes
`case' sample pairs displayed differential methylation (\selectlanguage{greek}β-\selectlanguage{english}value [?]0.2)
(\textbf{Figure 1.} Hierarchical clustering and heatmap).

The most promising biomarkers were identified on the basis of
differential methylation in the same direction (all either hyper- or
hypo-methylated in PCOS control relative to PCOS with diabetes cases) in
five or six (out of six) matched pairs. These criteria identified 19
putative methylation biomarkers (cg identifier list in \textbf{Table S2}
). These CpG loci were validated using Pyrosequencing in all 12 array
samples (Primer sequences in \textbf{Table S3} ). Methylation in three
of the candidates (cg11897887, cg02819655, and cg25542007) showed the
best concordance with corresponding array \selectlanguage{greek}β-\selectlanguage{english}values, and, upon visual
inspection of plotted methylation data, most clearly differentiated
cases from controls (\textbf{Figure 2} ).

\emph{Discussion}

\emph{Main findings.} This study demonstrates that there are measurable
differences in patterns of DNA methylation at initial diagnosis of PCOS,
between women who do, or do not, later develop diabetes. Genome-wide
methylation array screening identified 273 differentially methylated
sites in at least five of six case-control pairs. 19 CpG sites were
differentially methylated in the same direction, separating best the
women with PCOS and those that developed diabetes, and thus are the best
targets for further investigation as potential at-diagnosis prognostic
biomarkers.

\emph{Strengths.} To identify potential clinically useful prognostic DNA
methylation biomarkers, we used peripheral blood samples taken from
women at their initial diagnosis of PCOS. These samples had accompanying
long-term clinical follow-up data (up to 12 years), which enabled direct
comparison of `at-diagnosis' methylation patterns in women with PCOS who
did or did not later develop diabetes. We reasoned that the best
potential methylation biomarkers would be detectable at this early stage
where dietary, exercise, and weight management interventions are most
likely to be effective{[}5{]}.

We used well-established methods of genome-wide methylation assessment
(HumanMethylation450 arrays) to identify differences between women with
PCOS, and women with PCOS who later developed diabetes. Similar to
previous array-based prognostic marker screening studies, and to reduce
the likelihood of false positive targets, we used a [?]0.2 \selectlanguage{greek}β-\selectlanguage{english}value
difference to define differential methylation{[}9{]}.

\emph{Limitations} . Given the number of samples evaluated and the
variation in criteria used to define PCOS (and therefore variable
population prevalence), we cannot estimate prognostic or predictive
values. Furthermore, plotted Pyrosequencing data raise the possibility
that single nucleotide polymorphisms (SNPs) may be implicated in
methylation patterns observed in some of our candidates. Although known
SNPs were excluded during array filtering, polymorphic CpG sequences (in
all Infinium methylation arrays) necessitate caution in interpretation
of results{[}11{]}.

\emph{Interpretation.} Although there are no striking visual differences
between cases and controls in the heatmap of the differentially
methylated CpG sites, clustering reassuringly separately grouped the
women with PCOS who developed diabetes from the women that did not. The
273 differentially methylated CpG sites and the 19 putative biomarker
candidates identified is broadly in keeping with numbers in previous
methylation biomarker studies in benign tissue types{[}10{]}, but is
difficult to interpret in the context of lack of similar prior studies
in PCOS. Notwithstanding, methylation differences were substantiated by
Pyrosequencing in our candidates, confirming `proof of principle' that
DNA methylation holds the exciting potential as a prognostic biomarker
in PCOS.

Whilst it cannot be implied from our data, it is possible that
differential methylation at one or more of the sites identified may be
contributory to diabetes development in women with PCOS. Further studies
in larger patient cohorts are required to confirm our initial findings,
evaluate the possible importance of SNPs in the differentially
methylated sites identified, and to facilitate the mechanistic
investigation of DNA methylation changes in women with PCOS who develop
diabetes.

\emph{Conclusion}

We have presented the first 450K DNA methylation array assessment of
women with PCOS compared at initial diagnosis to women with PCOS who
later developed diabetes. Despite the limitations of a small sample
study, we have identified multiple loci of differential DNA methylation
between these clinical phenotypes. We suggest that DNA methylation holds
exciting potential for identifying clinically-usable prognostic
biomarkers that may help identify women likely to develop diabetes at an
early stage where lifestyle interventions may be effective in reducing
risks of diabetes and other complications of PCOS.



\textbf{Ethical approval}

West Midlands (Black Country) Research Ethics Committee 15/WM/0162

\textbf{Funding}

This work was supported by the University Hospitals of North Midlands
Charitable Trust.

\textbf{Acknowledgements}

Nil

\textbf{Disclosure of Interests}

Nil for all authors

\textbf{Contribution to Authorship}

Concept and design of study: MK, AAF, PW

Acquisition of data, or analysis and interpretation of data: MK, AM,
AAF, PW

Drafting the article or revising it critically for intellectual content:
MK, AAF, PW

Final approval of the version to be published: MK, AM, AAF, PW



\textbf{Figure Legends.}

\emph{Figure 1. Hierarchical clustering and heatmap of the 273
differentially methylated CpG sites.}

\selectlanguage{greek}\emph{Heatmap of the differentially methylated CpG sites identified by
array analysis between the clinical phenotypes of polycystic ovarian
syndrome (control) and polycystic ovarian syndrome with diabetes
(cases). The heatmap clusters/separates the six control samples on the
left, from the six case samples on the right. Each row represents an
individual CpG site, and each column represents an individual sample
(listed above the heatmap). The colour scale beneath represents
methylation status: unmethylated is green (β value = 0.0) and fully
methylated is red (β value = 1.0).}\selectlanguage{english}

\emph{Figure 2. Pyrosequencing of biomarker candidates.}

\emph{Pyrosequencing methylation values (y axis; 0-100\%) for all array
samples at cg11897887 (left), cg02819655 (middle), and cg25542007
(right), plotted as each matched-pair (x-axis). Each control in the pair
is represented by blue triangles, and each case in the pair is
represented by an orange diamond.}

\emph{(Pair 1: samples 2 and 16; pair 2: samples 3 and 7; pair 3:
samples 5 and 6; pair 4: samples 8 and 15; pair 5: samples 10 and 11;
pair 6: samples 13 and 12.)}

\emph{}

\textbf{References}

1. Sirmans SM and Pate KA (2013) Epidemiology, diagnosis, and management
of polycystic ovary syndrome. Clin Epidemiol 6: p. 1-13. doi:
10.2147/CLEP.S37559.

2. Livadas S and Diamanti-Kandarakis E (2013) Polycystic ovary syndrome:
definitions, phenotypes and diagnostic approach. Front Horm Res 40: p.
1-21. doi: 10.1159/000341673.

3. Lakhani K, Prelevic GM, Seifalian AM, Atiomo WU, and Hardiman P
(2004) Polycystic ovary syndrome, diabetes and cardiovascular disease:
risks and risk factors. J Obstet Gynaecol 24(6): p. 613-21. doi:
10.1080/01443610400007810.

4. Moran LJ, Misso ML, Wild RA, and Norman RJ (2010) Impaired glucose
tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary
syndrome: a systematic review and meta-analysis. Hum Reprod Update
16(4): p. 347-63. doi: 10.1093/humupd/dmq001

5. Anwar S and Shikalgar N (2017) Prevention of type 2 diabetes mellitus
in polycystic ovary syndrome: A review. Diabetes Metab Syndr Dec;11
Suppl 2:S913-S917. doi: 10.1016/ j.dsx.2017.07.015.

6. Polak K, Czyzyk A, Simoncini T, Meczekalski B (2017) New markers of
insulin resistance in polycystic ovary syndrome. J Endocrinol Invest
40(1): 1--8. DOI: 10.1007/s40618-016-0523-8.

7. Jacobsen VM et al. (2019) Epigenetic association analysis of clinical
sub-phenotypes in patients with polycystic ovary syndrome (PCOS).
Gynecol Endocrinol Aug;35(8):691-694. doi:
10.1080/09513590.2019.1576617.

8. Toperoff G et al. (2012) Genome-wide survey reveals predisposing
diabetes type 2-related DNA methylation variations in human peripheral
blood. Hum Mol Genet 21(2): p. 371-83. DOI: 10.1093/hmg/ddr472

9. Wessely F and Emes RD (2012) Identification of DNA methylation
biomarkers from Infinium arrays. Front Genet 3: p. 161. doi:
10.3389/fgene.2012.00161

10. Soler-Botija C, Galvez-Monton C, and Bayes-Genis A (2019) Epigenetic
Biomarkers in Cardiovascular Diseases. Front Genet 2019 Oct 9;10:950.
doi: 10.3389/fgene.2019.00950

11. Chen Y-A et al. (2013) Discovery of cross-reactive probes and
polymorphic CpGs in the Illumina Infinium HumanMethylation450
microarray. Epigenetics Feb;8(2):203-9. doi: 10.4161/epi.23470.

\textbf{Hosted file}

\verb`PCOS Figure 1.pptx`  available at \url{https://authorea.com/users/731000/articles/710320-novel-humanmethylatyion450-analyses-reveal-dna-methylation-predictors-of-future-diabetes-onset-in-women-with-polycystic-ovarian-syndrome}

\textbf{Hosted file}

\verb`PCOS Figure 2.pptx`  available at \url{https://authorea.com/users/731000/articles/710320-novel-humanmethylatyion450-analyses-reveal-dna-methylation-predictors-of-future-diabetes-onset-in-women-with-polycystic-ovarian-syndrome}

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