2019

UNIVERSIDADE DE LISBOA

FACULDADE DE CIÊNCIAS

DEPARTAMENTO DE BIOLOGIA ANIMAL

Metabolomics of Menopause in HIV-infected women

Cláudia Alexandra Ferreira Nunes

Mestrado em Biologia Humana e Ambiente

Dissertação orientada por:

Doutora Judit Morello Bullón

Professora Doutora Deodália Dias

II

III

Agradecimentos

O apoio e a contribuição de inúmeras pessoas foram imprescindíveis para o desenvolvimento

desta dissertação e por isso aqui deixo os meus sinceros agradecimentos.

Primeiramente, agradeço à Doutora Judit Morello Bullón, pela orientação prestada ao longo

desta dissertação, todo o apoio e disponibilidade que me prestou e por me ter introduzido ao mundo da

metabolómica.

À Professora Doutora Sofia de Azeredo Pereira, por me ter integrado no grupo de Farmacologia

do Centro de Estudos de Doenças Crónicas (CEDOC) e por toda a simpatia, apoio e ensinamentos que

me transmitiu.

À Professora Doutora Alexandra Maria Moita Antunes, por me receber no Centro de Química

Estrutural do Instituto Superior Técnico e a todo o seu apoio e explicações de química.

Sem esquecer da Dra. Umbelina Caixas, por toda a sua extrema simpatia, ajuda e disponibilidade

e por me ter dado a conhecer um pouco do seu mundo clínico.

À Professora Doutora Deodália Dias, por ter aceite ser minha orientadora ao longo deste ano e

por toda a atenção prestada.

Agradeço também ao grupo de Farmacologia do CEDOC, Catarina, João, Nuno e Clara por me

integrarem no grupo e por toda a ajuda que me prestaram no laboratório.

À minha colega de tese, Sara Martins, por me ouvir e me apoiar todos os dias; à sua companhia

e a todas as nossas conversas geek.

Aos meus pais pelo constante apoio e por sempre acreditarem em mim.

Aos meus irmãos por ouvirem sempre os meus desabafos, mesmo que não queiram muito.

Às minhas amigas, Ana Filipa, Andreia, Correia, Gracinha, Natacha e Vanessa, que apesar de

estarem longe, estão sempre presentes para tudo!

Às minhas 친구 s por também me acompanharem e me apoiarem durante este período.

E todas as demais pessoas que estiveram presentes e que me apoiaram de qualquer forma ao

longo desta etapa.

Obrigada a todos!

“Dream 결국 시련의

끝에 만개하리

Dream 시작은 미약할지언정

끝은 창대하리”

- 민윤기

IV

Agradeço também ao Roteiro Português de Infraestruturas de Pesquisa Científica de Interesse

Estratégico (categoria 1 – ref ROTEIRO/0028/2013) pelo acesso ao nó da RNEM do IST (RNEM-

LISBOA-01-0145-FEDER-022125). Este trabalho foi apoiado da Fundação para a Ciência e Tecnologia

(FCT) através dos projetos UID/QUI/00100/2019 (para o Centro de Química Estrutural) e

UID/MULTI/04046/2019.

V

Abstract

The reproductive aging process in females consists in three major phases: the premenopause

corresponding to the reproductive years, the perimenopause that consists in the transition to the final

phase, the postmenopause that begins at menopause diagnosis.

Postmenopause is known for both its distinctive hormonal and metabolic changes, primarily the

well-known estrogen loss. This change triggers the loss of the protective effects that estrogen confers to

women and raises the risk for the development of diseases, specifically, cardiovascular diseases by lipid

profile alterations. This status has been mainly studied through assessments of differences between

premenopause and postmenopause. However, it is reported that most of these changes occur earlier,

especially during late perimenopause. For this reason, studies between perimenopause and

postmenopause are of interest to better assess this transition.

HIV infection results in the loss of cell-mediated immunity, which favors the acquisition of

opportunistic infections that will irremediably cause the death of the patient. It is reported that over 32

million people have died due to HIV and approximately 37.9 million currently live with HIV-infection.

However, thanks to the development and introduction of antiretroviral therapies (ART), which have

shown to be effective on the control of the virus and the prevention of transmission to other individuals,

HIV-infected patients experienced a great increase in both their quality of life and their life expectancy.

Compared to the late 1990’s and early 2000’s, HIV-infected patients can now live over 50 years old and

approach the same life expectancy as non-HIV individuals. However, HIV-infected patients now face

new threats. HIV-infection, chronic inflammation and ART increases the risk of metabolic,

cardiometabolic, neurocognitive and bone diseases due to their potential adverse effects.

So, HIV-infected women adherent to antiretroviral therapy, have an increased life expectancy,

which means that they are able to live long enough to reach menopause. This way, and additionally to

their HIV and ART-inherent risk of disease, these women are now also susceptible to menopause-driven

age-related diseases.

In this study, we assessed the urinary metabolic profile of 74 HIV-infected women with similar

chronological ages and close to the menopause (from 45 to 49 years old). The menopausal status of

these women was defined according to their Antimüllerian hormone (AMH) levels (postmenopause and

three consecutive groups of perimenopause, from closest to furthest from menopause onset). A non-

targeted liquid chromatography and mass spectrometry-based metabolomics approach was conducted

to assess the impact of the menopausal status on the metabolic profile.

No metabolic differences were found between the postmenopause and the group of women

furthest from menopause. However, metabolic differences between the furthest and closest groups from

menopause were identified. These results prove that the most relevant metabolic changes can happen in

late perimenopause. However, the metabolites responsible for the observed differences could not be

identified and thus, further studies are required to corroborate these results.

Keywords: Menopause, Human Immunodeficiency Virus, Metabolomics, Antimüllerian hormone,

Antiretroviral drugs

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VII

Sumário

A vida das mulheres é caracterizada por grandes mudanças hormonais e metabólicas que

apresentam um grande impacto na suscetibilidade a certas doenças. Destas mudanças, a menopausa é

considerada como a mais distinta.

O processo de envelhecimento reprodutivo feminino consiste em três fases: a pré-menopausa,

correspondente aos anos reprodutivos da vida de uma mulher; a perimenopausa, que consiste na

transição para a fase final, a pós-menopausa que se inicia após o diagnóstico da menopausa.

A perimenopausa e a pós-menopausa são assinaladas pelas principais mudanças hormonais e

metabólicas que mais ameaçam a saúde de uma mulher. A pós-menopausa descreve várias alterações,

sendo a mais relevante, a perda de estrogénio que anteriormente fornecia um efeito protetor à saúde

feminina e que pode contribuir para o risco do desenvolvimento de doenças, principalmente, as

cardiovasculares. Os fatores de risco que mais são descritos na pós-menopausa são os elevados níveis

de colesterol total, lipoproteínas de baixa densidade (LDL), triglicéridos e apolipoproteínas-B (apoB).

Contudo, estas alterações não ocorrem apenas aquando do evento da menopausa. Estas têm

início e ocorrem gradualmente durante a perimenopausa, mais especificamente durante a fase final da

perimenopausa, até uma estabilização após o evento da menopausa. Esta fase é definida por ciclos

menstruais irregulares, mais longos e a presença de meses amenorreicos.

Deste modo, tendo em conta a presença de alterações metabólicas ainda na fase anterior ao

evento da menopausa, um estudo extensivo da perimenopausa pode contribuir para a prevenção de

doenças futuras associadas a estes sintomas.

VIH (vírus da imunodeficiência humana) é um retrovírus com particular tropismo para o sistema

imunitário, conduzindo à imunossupressão. Esta condição propicia o desenvolvimento de infeções

comumente chamadas de infeções oportunistas que podem conduzir à morte.

Até aos dias de hoje, o VIH já causou a morte de mais de 32 milhões de pessoas e atualmente

cerca de 37.9 milhões vivem com infeção VIH. Contudo, a terapêutica antirretroviral de alta eficácia

(TAR) veio mudar o paradigma da infeção VIH que passou de potencialmente fatal a uma infeção

crónica.

O estudo e desenvolvimento dos antirretrovirais permitiu o aparecimento de fármacos não só

eficazes, mas também menos tóxicos e melhor tolerados. A eficácia dos atuais regimes antirretrovirais

mostrou o controlo da infeção assim como o controlo da transmissão do vírus. No entanto, a TAR,

juntamente com outros fatores como o próprio vírus, hábitos ou estilos de vida do individuo e fatores

ambientais e/ou hereditários contribuem para o risco do desenvolvimento de comorbilidades como

diabetes, stress oxidativo, aterosclerose, dislipidemia, osteopenia, hipogonadismo e inflamação. Ainda

assim, o aumento da esperança média de vida dos indivíduos com infeção VIH foi apenas possível com

o apoio destas terapias. Comparativamente com os últimos anos da década de 1990 e início dos anos

2000, pessoas infetadas por VIH com 20 anos, viviam em média até cerca dos 30 anos de idade e

atualmente vivem para além dos 50 anos e cada vez mais se aproximam da esperança média de vida de

pessoas saudáveis.

Deste modo, as mulheres com infeção HIV vivem para além da menopausa e por isso

apresentam um risco acrescido de doenças relacionadas com a idade. Tendo em conta este risco, esta

população é assim alvo para estudos de análise de possíveis biomarcadores destas doenças, assim como

VIII

marcadores de progressão das mesmas, para que deste modo possibilitem o desenvolvimento de métodos

preventivos.

Metabolómica é um método de análise que avalia um amplo grupo de metabolitos com o

objetivo de a estabelecer relações entre o fenótipo e o metabolismo. Este método consiste na análise

química de fluídos biológicos através de plataformas como a espectrometria de massa ou espectroscopia

por ressonância magnética nuclear de modo a identificar metabolitos (pequenas moléculas) nas amostras.

Por esta razão, a metabolómica é um método amplamente usado para o estudo de biomarcadores e alvos

de doenças.

Neste estudo foi traçado o perfil metabolómico na urina de 74 mulheres com infeção HIV entre

os 45 e os 49 anos de idade. O estádio da menopausa foi classificado de acordo com os níveis da hormona

antimülleriana (AMH). Assim, 54 mulheres foram classificadas em três estádios da perimenopausa

(grupo B, o grupo mais aproximado ao evento da menopausa e grupos C e D, grupos gradualmente mais

afastados do evento) e as restantes 20 como pós-menopáusicas (grupo A).

Deste modo, tendo em conta estes grupos, avaliámos o perfil metabólico das urinas adotando

uma abordagem metabolómica não direcionada onde as amostras foram analisadas através de

cromatografia líquida e subsequente espectrometria de massa de modo a avaliar o impacto do estado da

menopausa no perfil metabólico.

As diferenças nos resultados da PCA entre o grupo A, mulheres pós-menopáusicas, e o grupo

D, mulheres mais afastadas da menopausa, eram espectáveis por apresentarem a maior diferença de

idade biológica entre todos os nossos grupos, no entanto estas não foram confirmadas.

Contudo, os resultados entre os grupos B e D demonstraram que existe uma influência da AMH

no perfil metabólico, ou seja, diferenças entre os perfis metabólicos das mulheres mais próximas do

evento da menopausa e das mulheres mais afastadas do evento foram visíveis, confirmando assim as

mudanças metabólicas que ocorrem na fase final da perimenopausa. No entanto, não foi possível a

confirmação da identificação de nenhum destes metabolitos.

Em suma, este estudo aplicou métodos metabolómicos para avaliar os perfis metabólicos de

mulheres com infeção HIV especificamente na sua transição para a menopausa. Não temos

conhecimento de um estudo prévio com estas características. Apesar de não termos conseguido

identificar possíveis metabolitos como biomarcadores, concluímos, através dos dados metabolómicos,

a existência de diferenças na transição da perimenopausa para a menopausa que confirmam a diferença

de idades biológicas das mulheres em estudo, ainda que apresentem idades cronológicas aproximadas.

Palavras chave: Menopausa, Vírus da Imunodeficiência Humana, Metabolómica, Hormona

Antimülleriana

IX

X

Index

Agradecimentos.................................................................................................................................... III

Abstract ................................................................................................................................................. V

Sumário .............................................................................................................................................. VII

List of Figures and Tables ................................................................................................................... XI

List of abbreviations and acronyms ................................................................................................... XIII

1 Introduction ......................................................................................................................................... 1

1.1 Menopause ................................................................................................................................... 1

1.2 Antimüllerian hormone ................................................................................................................ 2

1.3 Metabolic changes associated to menopause ................................................................................ 3

1.4 Brief introduction on HIV ............................................................................................................ 4

1.5 Metabolic changes due to HIV infection ...................................................................................... 4

1.6 Metabolic changes due to ARVs .................................................................................................. 5

1.7 Higher risk of age-related diseases in menopausal HIV-infected women ..................................... 5

1.8 Metabolomics ............................................................................................................................... 6

1.9 Rationale ...................................................................................................................................... 9

2 Materials and Methods ...................................................................................................................... 10

2.1 Clinical samples ......................................................................................................................... 10

2.2 Hormone quantification .............................................................................................................. 11

2.3 Urine treatment ........................................................................................................................... 11

2.4 Sample acquisition ..................................................................................................................... 11

2.5 Validation of LC-MS analyses ................................................................................................... 12

2.6 Data preprocessing ..................................................................................................................... 12

2.7 Statistical analyses ...................................................................................................................... 13

2.7.1 Statistical analyses of clinical data ...................................................................................... 13

2.7.2 Statistical analyses of metabolomics data ............................................................................ 13

2.8 Metabolites identification ........................................................................................................... 14

3 Results ............................................................................................................................................... 15

3.1 Characterization of the population .............................................................................................. 15

3.2 Quality of the data ...................................................................................................................... 16

3.3. Influence of race, menopause, ARV and hormone levels in the metabolic profile .................... 19

3.4 Metabolite identification ............................................................................................................ 24

3.5 Analysis of saturation in samples ............................................................................................... 25

4 Discussion ......................................................................................................................................... 36

4.1 Conclusions ................................................................................................................................ 38

References ............................................................................................................................................ 39

Annexes................................................................................................................................................ 47

XI

List of Figures and Tables

Figure 1.1 Hormone levels throughout menstrual cycle phases;

Figure 1.2 Schematic visualization of HIV-infection, ART and ageing outcomes in the human

physiology and metabolism;

Figure 2.1 Data preprocessing steps;

Figure 3.1 Hormone levels in each AMH group;

Figure 3.2 Example of a saturated ion present in the LC-MS chromatograms found at approximately 3

min of retention time;

Figure 3.3 PCA score plot of total area normalized data and PQN data of all samples;

Figure 3.4 PCA score plot of total area normalized data and PQN data of all samples colored by

acquisition day;

Figure 3.5 PCA plot score colored by race;

Figure 3.6 PCA score plot colored by AMH group;

Figure 3.7 PCA score plot of samples with identified ART regimens colored by principal ART;

Figure 3.8 PCA score plot of samples with identified ART regimens colored by backbone ART;

Figure 3.9 PCA scores plot of AMH group combinations;

Figure 3.10 Cross-validated score plot comparing AMH groups B and D;

Figure 3.11 p(corr)/VIP score plot; Red circles correspond to the selected ions;

Figure 3.12 MS/MS spectra of the ions that matched online metabolomic data and correspondent

chemical structures of their fragments;

Figure S.1 Chromatographic representation of double peaks.

Table 1.1 Nomenclature of the stages/events of reproductive ageing;

Table 1.2 Metabolomic studies performed on menopause and its different stages;

Table 1.3 Metabolomic studies performed on HIV infection;

Table 2.1 Groups of women according to AMH levels;

Table 2.2 AMH values in healthy women in ng/mL;

XII

Table 3.1 Characterization of the population in study; groups are defined by AMH values (in ng/mL);

Table 3.2 Potential identifications searched in online databases for the ions selected by the p(corr)/VIP

score plot;

Table 3.3 Potential identifications for the saturated ions (characterized by a minimum intensity of 1x107);

Table S.1 Validation of LC-MS analyses.

XIII

List of abbreviations and acronyms

3TC – lamivudine

ABC – abacavir

AIDS – acquired immunodeficiency syndrome

AMH – Antimüllerian hormone

apoB – apoprotein B

ART – antiretroviral therapy

ARV - antiretroviral

ATV – atazanavir

BMD – bone mass density

CHD – coronary heart disease

CV – coefficient of variation

CVD – cardiovascular disease

DRV – darunavir

E2 – estradiol

EI – entry inhibitor

FMP – final menstrual period

FSH – follicular stimulating hormone

FTC – emtricitabine

GC-MS – gas chromatography-mass spectrometry

HDL – high-density lipoprotein

HIV – human immunodeficiency virus

II – integrase inhibitor

LC-MS – liquid chromatography-mass spectrometry

LDL – low-density lipoprotein

LH – luteinizing hormone

LLQ – lower limit of quantification

MS – mass spectrometry

NMR – nuclear magnetic resonance

NNRTI – non-nucleoside reverse transcriptase inhibitor

NRTI – nucleoside reverse transcriptase inhibitor

NVP – nevirapine

PCA – principal component analysis

XIV

PI – protease inhibitor

PLS – partial least squares

PLS-DA – partial least squares

PQN – probabilistic quotient normalization

QC pool – quality control pool

RAL – raltegravir

TC – total cholesterol

TDF – tenofovir

TG – triglycerides

1

1 Introduction

1.1 Menopause

A woman’s life is characterized by major shifts at hormonal levels which comprehend three

different hormonal phases: the premenopause, the menopause transition and the postmenopause. At

menarche, the reproductive years begin followed by the menopause transition where menstrual and

hormonal irregularities occur. Menstrual irregularities keep happening until the final menstrual period

(FMP) and after 12 months of amenorrhea, menopause is diagnosed and thus postmenopause stage

begins. Perimenopause is the stage that comprehends both menopause transition and the 12-month

amenorrhea period (Table 1.1) (Monteleone, Mascagni, Giannini, Genazzani, & Simoncini, 2018;

Polotsky & Polotsky, 2010).

Table 1.1 Nomenclature of the stages/events of reproductive ageing (adapted from Monteleone et al., 2018); FMP, final

menstrual period.

Men

arch

e

Early

reproductive

years

Late

reproductive

years

Ear

ly m

enopau

se

tran

siti

on

Lat

e m

enopau

se

tran

siti

on

FM

P +

12

-month

amen

orr

hea

Early

postmenopause

Late

postmenopause

Premenopause Perimenopause Postmenopause

Beginning at menarche, the premenopause is described as the woman’s reproductive years

where ovulation occurs. Women are born with a set number of oocytes which are then released gradually

in each menstrual cycle. These are stimulated by a positive and negative feedback-regulated cycle of

hormones. Starting at mid follicular phase, follicular stimulating hormone (FSH) stimulates the

recruitment of antral follicles for further development and a rise of estradiol (E2) at the granulosa cells.

This rise of E2 halts the menses and the uterus lining thickens. At the ovary, follicles are developed, and

the selection of a dominant follicle is followed. The dominant follicle will then mature and a peak of

luteinizing hormone (LH), stimulated by FSH, will release the mature egg from the ovarian follicle. E2

later suppresses LH and stimulates the proliferation of the endometrium (Figure 1.1). To negatively

feedback the FSH, inhibin B is secreted by oocytes inhibiting FSH synthesis at the pituitary (Draper et

al., 2018; Hayes, Hall, Boepple, & Crowley, 1998).

Due to aging, the number of oocytes decreases and consequently the amount of inhibin B also

decreases, and thus, without its negative feedback, FSH levels conversely increase (Monteleone et al.,

2018). These hormonal changes depict the early stages of the menopausal transition, known as

perimenopause (Polotsky & Polotsky, 2010). In this phase, the number of ovarian follicles keeps

decreasing and consequently the levels of estrogen slowly decrease while the levels of FSH keep rising.

Some of the physical symptoms of the perimenopause are menstrual irregularities with increasingly

longer stages of amenorrhea, hot flashes, night sweats and sleep disorders (Monteleone et al., 2018;

Santoro & Randolph, 2011).

At the near exhaustion of ovarian follicles, the ovary cannot respond to the rapidly rising levels

of FSH and leads to a sharp decline in estrogen levels (Randolph et al., 2011; Santoro & Randolph,

2011). These hormonal shifts and loss of ovarian follicular function at the perimenopause lead to the

ultimate event in the female body: menopause (Polotsky & Polotsky, 2010).

2

Menopause is clinically diagnosed after 12 months of amenorrhea and typically sets between 45

and 60 years old with a mean age of approximately 51 years of age (Calvet et al., 2015).

Proceeding this event, postmenopause is established, which is characterized by low levels of

estrogen and high levels of FSH and includes numerous symptoms such as vaginal dryness, vulvovaginal

atrophy, lower urinary tract symptoms and dyspareunia, which can be relieved through hormonal

treatments (Chada et al., 2003; Polotsky & Polotsky, 2010; Takahashi & Johnson, 2015).

1.2 Antimüllerian hormone

Antimüllerian hormone (AMH) has been mainly associated to the early male development,

specifically to their sexual differentiation at fetal stage (approximately 7th week of gestation). Reversely,

in females, AMH production has only been detected at the final stages of gestation. The absence of

AMH throughout gestation promotes the Müller ducts development and thus the differentiation of the

female reproductive system structures.

In females, AMH is secreted the highest by the granulosa cells in the preantral and small antral

follicles of the ovary during reproductive years. At the end of gestation, AMH is produced in very low

quantities, however it increases reasonably after birth until its peak at approximately 25 years of age

(Oh, Choe, & Cho, 2019). Once the female reaches menarche and the menstrual cycles begin, the AMH

has the important function of inhibiting the follicle growth by restraining the effects of FSH. This

inhibition limits the recruitment and subsequent growth of primordial follicles, therefore preserving the

ovarian reserve. Furthermore, AMH concentrations can be measured in both serum and urine and so,

over the years, AMH physiology and clinical utility has been extensively studied and was found to

exhibit a direct correlation to the number of primordial follicles in the ovary. For this reason, AMH is

currently used in the evaluation of several conditions such as predicting the ovarian response to

hyperstimulation of the ovaries for IVF, assessing damage to the ovarian follicle reserve, assessing

Figure 1.1 Hormone levels throughout menstrual cycle phases (Draper et al., 2018).

3

polycystic ovary syndrome condition and predicting the age of menopause (de Kat et al., 2019; Dewailly

et al., 2014; A. La Marca & Volpe, 2006).

In this study, we used AMH as an assessment parameter for menopausal stages (premenopause,

perimenopause and postmenopause) that, contrarily to FSH and inhibin B, has shown to be stable

throughout the menstrual cycle and is not influenced by the presence of oral contraceptives (Bleil,

Gregorich, McConnell, Rosen, & Cedars, 2013; Oh et al., 2019).

1.3 Metabolic changes associated to menopause

Beyond the major physical symptoms of postmenopause, numerous metabolic changes also

occur in the menopausal onset period which can lead to severe conditions. These changes occur

predominantly between 45 and 50 years old, corresponding to the menopause transition, and primarily

contribute to high risks of cardiovascular diseases (CVD) (Auro et al., 2014).

The loss of estrogen is one of the major changes. Estrogen expresses numerous protective effects

by interacting with various pathways in female metabolism. It is reported that estrogen improves the

vascular integrity reducing permeability to LDLs, increases endothelial cell survival, inhibits reactive

oxygen species (ROS), participates in the regulation of angiogenesis and inhibits inflammation (Fortini

et al., 2019). So, with the loss of estrogen and the subsequent loss of these effects, women become more

susceptible to cardiovascular complications namely myocardial infarction and stroke, renal diseases,

osteoporosis and overall mortality (Fortini et al., 2019; Maric-Bilkan, Gilbert, & Ryan, 2014; Souza &

Tezini, 2013). Atherogenic metabolites were found to increase rapidly at menopause transition through

oxidative stress, changes in the lipid profile and endothelial dysfunction, thus demonstrating the higher

prevalence of atherosclerosis with menopause onset (Auro et al., 2014; Polotsky & Polotsky, 2010;

Wang et al., 2018).

Postmenopausal women present higher levels in total cholesterol (TC), triglycerides (TG), low-

density lipoprotein (LDL) and apolipoprotein-B (apoB). Moreover, it has been reported that both total

and LDL cholesterol tend to increase while HDL cholesterol declines among women who stopped

menstruating for at least 1 year in comparison to women around the same age who continued

menstruating. This loss of protection provided by HDL compromises postmenopausal women’s health

with higher CVD risk independently of age. Furthermore, the high levels of TC, TG, LDL and apoB can

promote hyperglycemic environments (diabetes leading condition), which can pose even greater risk of

oxidative damage (Auro et al., 2014; Carr, 2003; Stachowiak, Pertyński, & Pertyńska-Marczewska,

2015).

In addition, it is important to remark that, although postmenopausal women present these lipid

alterations, most of these changes occur majorly in late perimenopause, specifically proatherogenic lipid

changes (Auro et al., 2014; Derby et al., 2009; Polotsky & Polotsky, 2010). The scarce number of studies

on metabolic changes through perimenopause into postmenopause strengthens the need to study these

stages to completely understand menopausal transition.

In sum, women spend a large percentage of their life in postmenopause experiencing

inflammation and several alterations in their lipid profile that can compromise the metabolic profile

causing it to be more proatherogenic and proinflammatory independently of age and thus greatly

increasing the risk of developing numerous diseases and, ultimately, death (Polotsky & Polotsky, 2010;

Wang et al., 2018).

4

1.4 Brief introduction on HIV

Human immunodeficiency virus (HIV) is a retrovirus that targets vital cells of the human

immune system, specifically the CD4+ T cells, macrophages and dendritic cells (Cunningham, Donaghy,

Harman, Kim, & Turville, 2010; Pedro, Henderson, & Agosto, 2019). This infection has caused over 32

million deaths and approximately 37.9 million currently live with HIV (World Health Organization,

2019).

HIV infection of CD4+ cells has been described as a chronic cycle of infection. This designation

was applied considering that once infected, these cells die through pyroptosis that consequently

promotes inflammation, which in its turn recruits more CD4+ cells that ultimately also get HIV infected

(Doitsh et al., 2014; Doitsh & Greene, 2016). The repercussion of this chronic cycle of infection and

inflammation is a decrease in the CD4+ cell count that progresses to acquired immune deficiency

syndrome (AIDS). The condition of AIDS then describes a weakened immune system that favors the

growth of opportunistic infections (J. P. Moore, 1997).

In order to control HIV infection, highly active antiretroviral therapy (ART) was introduced.

ART consists on the combination of at least three antiretrovirals (ARVs) that operate in different steps

of the virus life cycle. For identification purposes, the ARVs are grouped into five main classes

according to their mechanism of action: nucleoside reverse transcriptase inhibitors (NRTIs) and non-

nucleoside reverse transcriptase inhibitors (NNRTIs) inhibit the activity of reverse transcriptase (Das &

Arnold, 2013); protease inhibitors (PIs) bind to the viral proteases thus inhibiting the virus replication

(Wensing, van Maarseveen, & Nijhuis, 2010); integrase inhibitors (IIs) inhibit the viral integrase that is

responsible for integrating the virus DNA into the target cell DNA (Métifiot, Marchand, & Pommier,

2013) and entry inhibitors (EIs) which inhibit the binding of the HIV virion to the human cell (Margolis,

Heverling, Pham, & Stolbach, 2014). Hence, the ART’s main goal is to inhibit the virus replication,

increasing the number of CD4+ cells and decreasing the risk of opportunistic infections and overall death.

The adhesion to these therapies enabled the progress of HIV infection from an acute to a chronic illness

(R. D. Moore & Chaisson, 1999; Palmisano & Vella, 2011; Srinivasa & Grinspoon, 2014). The

introduction of ART regimens has so far increased lifestyle and life expectancy for people living with

HIV which has increased from 30 years old (as of late 1990’s and early 2000’s) to over 50 years old,

approaching the life expectancy of non-HIV infected individuals (Nakagawa, May, & Phillips, 2013).

1.5 Metabolic changes due to HIV infection

HIV infection poses a great threat to human health given its characteristics and general

symptoms. But, furthermore, it also implies other complications. Reports have shown that HIV-infected

people experience numerous metabolic changes such as increased levels of triglycerides, oxidative stress,

fatty acids and mitochondrial dysfunction, decreased levels of sphingomyelin, glutathione, taurine and

tryptophan and hypocholesterolemia (Cassol et al., 2013; Peltenburg et al., 2018; Williams, Koekemoer,

Lindeque, Reinecke, & Meyer, 2012).

Another major feature of HIV infection is chronic inflammation that derives from the

destruction of the gut mucosa by the virus. The microbial products then go to the liver causing damage

and impairing the organ to properly function at both microbial clearance and protein synthesis. This

contributes to a chronic recruitment of monocytes and systemic inflammation condition that leads to

end-organ disease, that is, all major organs that maintain their function through the circulatory system

(Deeks, Tracy, & Douek, 2013).

5

1.6 Metabolic changes due to ARVs

ART allows to suppress HIV viremia and recover CD4+ cells, which positively impacts HIV-

induced metabolic alterations. However, on the other side, ART associated toxicity can have negative

metabolic effects. HIV-infected patients on ART have been characterized by increased triglycerides,

increased total and LDL cholesterol, and decreased HDL cholesterol as consequence of ARV use

(Cassol et al., 2013), thus leading to the acknowledgement of characteristic metabolic alterations of each

ARV classes. NRTIs have reported to cause mitochondrial toxicity which can lead to several conditions

as renal and hepatic failures, pancreatitis and neuropathy; NNRTIs, neurological and hepatic alterations

and dyslipidemia; PIs, dyslipidemia with increased total cholesterol, triglycerides, and LDL which

elevates the susceptibility to cardiovascular diseases. Entry inhibitors have reported cases of

hyperglycemia and pancreatitis, and integrase inhibitors, renal failure (Margolis et al., 2014). For this

reason, ART, specifically PIs, are associated with atherosclerosis and insulin resistance (Avelino-Silva,

Ho, Avelino-Silva, & Santos, 2011; Bociaga-Jasik et al., 2014), and both NRTIs and PIs for inducing

oxidative stress, unregular adipogenesis and lipid metabolism, impaired glucose transport and lipolysis

(Srinivasa & Grinspoon, 2014).

It is known that HIV infection and ART regimes have led to metabolic disorders (Avelino-Silva

et al., 2011; Srinivasa & Grinspoon, 2014) and, together with the underlying chronic inflammation, these

may still pose threats to the human health through metabolic changes. So, HIV infection in association

with ART regimes have been highly related with several conditions such as diabetes mellitus, oxidative

stress, inflammation, atherosclerosis, dyslipidemia, osteopenia and cardiovascular diseases due to

metabolic and hormonal disorders. However, in an attempt to minimize these risks associated to ART,

ARVs have been highly improved and, although they demonstrate less toxicity, metabolic changes

persist and thus a non-AIDS mortality is still present (Ahmed, Roy, & Cassol, 2018; Avelino-Silva et

al., 2011; John, 2016).

1.7 Higher risk of age-related diseases in menopausal HIV-infected women

Since the life expectancy of HIV-infected women who follow antiretroviral therapy has

increased enough for them to be able to reach menopause, they are then faced with a combination of

multiple factors with a clear negative metabolic impact, namely the presence of HIV-infection, the ART

regimen, the aging process and the menopause transition and onset. Thus, HIV-infected women possess

a great risk of developing metabolic-related diseases such as bone, metabolic, renal and cardiovascular

diseases, namely osteopenia, osteoporosis, hypogonadism, diabetes mellitus, atherosclerosis,

dyslipidemia and overall inflammation. (Avelino-Silva et al., 2011; Fan, Maslow, Santoro, &

Schoenbaum, 2008; John, 2016; Nasi et al., 2017; Nicks et al., 2010), that develop as result of changes

at the lipid profile, fatty acids, oxidative stress, mitochondrial toxicity and losses of estrogen and HDL

protections that menopause provokes (Auro et al., 2014; Cassol et al., 2013; Derby et al., 2009; Fortini

et al., 2019; Polotsky & Polotsky, 2010; Stachowiak et al., 2015)

To summarize, HIV-infected women going through menopause are faced with numerous

accumulated risks that are heightened by ART, however, it is important to recall that the lack of these

therapies would stand as a death sentence, so more support must be provided for these women in order

to avoid a reduced adherence of or withdrawal from ART (Monteleone et al., 2018).

6

Figure 1.2 Schematic visualization of HIV-infection, ART and ageing outcomes in the human physiology and metabolism

(Nasi et al., 2017); cART, combination antiretroviral therapy.

1.8 Metabolomics

Metabolomics is a post-genomic technology that allows an unbiased investigation and

understanding of the mechanisms behind biological functions through the analysis of the metabolites

(small molecules) present in biological fluids and thus has been used to identify biomarkers and targets

of several diseases and conditions (Cassol et al., 2013; Cui et al., 2019; Woo et al., 2009).

To perform this type of analysis, different analytical chemistry platforms may be used such as

mass spectrometry (MS) and nuclear magnetic resonance (NMR). NMR is known for its high

reproducibility and MS for its high sensitivity and, although NMR provides nondestructive and

noninvasive characteristics, MS remains superior due to its high detection level, structural understanding

through parent and fragment ions and identification of compounds by spectral matching (Emwas, 2015;

Serkova & Little, 2014).

Metabolomics can be characterized into two categories: nontargeted and targeted analyses. The

nontargeted analyses aim to profile the total number of metabolites in a sample (metabolomic

fingerprinting). In their turn, the targeted analyses focus on the identification and quantification of

selected metabolites. These metabolites are often known, enabling the study of specific metabolic

pathways or even drug or food metabolism products (Emwas, 2015).

Metabolomic analyses of menopause or HIV infection have been performed using both MS and

NMR techniques as well as different statistical analysis methods. The main goal of those studies is to

find metabolic changes that occur in between premenopausal and postmenopausal women or between

HIV-infected patients and healthy individuals (Tables 1.2 and 1.3). These differences can then help

identify potential biomarkers of disease and drug targets (Cassol et al., 2013; Ghannoum et al., 2013;

Ke et al., 2015; Miyamoto et al., 2017).

However, to our knowledge, there are no studies addressing the metabolic changes of

menopause in HIV infected women. Nevertheless, it has shown to be of interest to assess this target

population due to their distinct condition.

7

Table 1.2 Metabolomic studies on menopause and its different stages; (UP)LC-MC, (ultra-performance) liquid chromatography-mass spectrometry; GC-MS, gas chromatography-mass

spectrometry; NMR, nuclear magnetic resonance; PCA, principal component analysis; PLS-DA, partial least squares – discriminant analysis; yo, years old.

Metabolomic studies on menopause

Reference Aims Study design Samples Age Analytical

technique Statistical analysis Findings

Cui et al.,

2019

Influence of menopause

on relationships between

186 biochemical

metabolites

Targeted

metabolomics

Plasma samples

▪ 55 premenopausal women

▪ 64 postmenopausal

women

21 to 74 yo UPLC-

MS

Pairwise Pearson’s

correlations

Menopause promotes

changes in the metabolism

of some phospholipids and

sphingolipids, acylcarnitine

and amino acids

Ke et al.,

2015

Discover potential

menopause biomarkers

Untargeted

metabolomics

Plasma samples

▪ 52 premenopausal women

▪ 63 postmenopausal

women

39 to 66 yo UPLC-

MS PCA and PLS-DA

28 metabolites were

identified as potential

biomarkers for menopause

Miyamoto et

al., 2017

Assess the metabolomic

profiles of low BMD on

postmenopausal women

Untargeted

metabolomics

Serum samples

47 postmenopausal women

39 to 64 yo LC-MS PCA

Low bone mass density

markers were found in

postmenopausal women

S. C. Moore

et al., 2018

Confirm association

between high BMI and

the high risk of breast

cancer in

postmenopausal women

Untargeted

metabolomics

Serum samples

▪ 621 postmenopausal

women

▪ 621 matched controls

55 to 74 yo Not

described

Partial Pearson’s

correlation and

conditional logistic

regression

This association was

confirmed through four

metabolites present in

metabolic pathways that

contribute to breast

carcinogenesis

Yu et al.,

2019

Find potential

biomarkers and assess

the metabolic pathways

involved in

postmenopausal

osteoporosis

Untargeted

metabolomics

Urine samples

322 women

40 to 62 yo GC-MS PCA and PLS-DA

The taurine and the β-

alanine metabolic pathways

were associated with the

pathology of

postmenopausal

osteoporosis

8

Table 1.3 Metabolomic studies on HIV infection; ART, antiretroviral therapy; CVD, cardiovascular diseases; CHD, coronary heart disease; LDL, low density lipoproteins, HDL, high density

lipoproteins; LC-MS, liquid chromatography-mass spectrometry; GC-MS, gas chromatography-mass spectrometry; NMR, nuclear magnetic resonance; PCA, principal component analysis; PLS-

DA, partial least squares – discriminant analysis.

Metabolomic studies on HIV

Reference Aims Study design Samples Analytical

technique

Statistical

analysis Findings

Cassol et al.,

2013

Identify metabolic differences

between HIV-infected persons

on ART and healthy controls

Untargeted

metabolomics

Plasma samples

▪ 32 HIV-infected on

ART (at least one PI)

▪ 20 healthy controls

LC/GC-

MS

PCA and PLS-

DA

35 metabolites were identified as

markers of inflammation, microbial

translocation, and hepatic function to

which were linked to lipid irregularities

Cassol,

Misra,

Dutta,

Morgello, &

Gabuzda,

2014

Identify changes at metabolic

pathways associated with

neurocognitive disorders

through comparisons between

HIV infected patients and HIV

negative controls

Untargeted

metabolomics

Cerebrospinal fluid

samples

▪ 46 HIV-infected patients

▪ 54 controls

LC/GC-

MS

PCA and PLS-

DA

Metabolites associated with

neurotransmitter production,

mitochondrial function, oxidative

stress and metabolic waste on HIV-

infected persons were identified

confirming the higher risk of

neurocognitive disorders

Duprez et

al., 2009

Find associations between

lipoproteins and CVD in HIV-

infected individuals

Targeted

metabolomics

Blood plasma

728 HIV-infected patients:

▪ 248 CVD cases

▪ 480 controls

NMR

Conditional

logistic

regression

The authors demonstrated that a lower

baseline of total, large and small HDL-

particles is related to CVD in HIV

infected patients

Swanson et

al., 2009

Characterize the lipid profile of

medically underserved HIV-

infected individuals

Targeted

metabolomics

Blood serum

132 HIV-infected

individuals

NMR One-way

ANOVA

Changes at lipidic profiles (specifically

LDL and HDL lipoproteins) demonstrate

a greater risk of CHD in medically

underserved HIV-infected individuals

Williams,

Koekemoer,

Lindeque,

Reinecke, &

Meyer, 2012

Detection of metabolic changes

during the 2nd stage HIV;

Identification of disease

progression markers for

prevention of future symptoms

Untargeted

metabolomics

Blood serum

▪ 18 2nd stage HIV-

infected individuals

without ART

▪ 21 healthy controls

GC–MS PCA and PLS-

DA

10 metabolites were linked to disrupted

mitochondrial metabolism, changes in

lipid metabolism and oxidative stress

9

1.9 Rationale

Several studies have already evaluated the metabolic differences between premenopausal and

postmenopausal women (Cui et al., 2019; Ke et al., 2015). However, few studies have evaluated the

specific metabolic changes that occur during the perimenopause, which was identified as the

menopausal period where most metabolic changes begin (Derby et al., 2009; Polotsky & Polotsky, 2010).

Thus, there’s a high need of studies focused on perimenopause in order to develop and increase

prevention to these diseases.

Since the survival rate of HIV-infected individuals is the highest ever, infected women can now

live long enough to experience menopause. However, HIV-infected women reaching menopause present

higher risk of metabolic alterations than non-HIV women due to the additive detrimental effects of HIV

and ARVs on the metabolism. These characteristics combine a fast ageing process on these women

which can be studied in order to assess disease progression as well as disease biomarkers.

Based on the information explored above, we hypothesized that there are metabolic differences

between HIV-infected women in postmenopause and HIV-infected people in perimenopause.

Thus, we performed a non-targeted metabolomic study using a liquid chromatography and mass

spectrometry platform in order to assess the impact of menopausal status according to AMH levels in a

population of HIV-infected women with similar chronological ages and close to menopause.

10

2 Materials and Methods

2.1 Clinical samples

Urine samples were collected at Centro Hospitalar de Lisboa Central, EPE and Hospital

Professor Doutor Fernando Fonseca, EPE and stored frozen at -80 ºC at CEDOC – Chronic Diseases

Research Center, UNL.

The study is part of a project that aims to find biomarkers at molecular level for the

cardiometabolic risk in human immunodeficiency virus (HIV)-infected women and its protocol was

approved by the Ethics Committees of the hospitals involved. All patients provided written informed

consent to participate. Inclusion criteria were defined as women with more than 18 years old and HIV-

infection with or without antiretroviral therapy.

Demographic, clinical and laboratory data collected for the study included: age, sex, ethnicity,

antiretroviral therapy (ART) regimens and concentrations of the hormones AMH, E2, FSH and LH. The

concentrations of the hormones were quantified previous to this study through specific essays described

in section II.2 Hormone quantification.

For this master’s project, in order to assess the metabolic differences between menopausal status

in HIV-women, we selected those women between 45 and 49 years old. The rationale for this selection

was 1) to avoid the influence of the chronological age; and 2) to focus on the women who are close to

the menopause. A total of 75 urine samples met these criteria. These 75 samples were then divided into

five groups (Table 1) according to the AMH reference values in healthy women of the same age (Table

2) (adapted from Wilson, Sabin, & Hartshorne, 2017).

Given that group E consisted on a single sample was thus excluded.

Table 2.1 Groups of women according to AMH levels; *Lower limit of quantification (LLQ).

AMH in healthy women (45-49

yo) (ng/mL) Group N

<0.010* A 23

<0.042 B 17

0.042-0.223 C 14

0.224-2.058 D 20

>2.058 E 1

75

Table 2.2 AMH values in healthy women (adapted from Wilson et al., 2017) in ng/mL; LLQ, lower limit of quantification.

AMH values (ng/mL)

10th percentile Median 90th percentile LLQ

20-24 years 1.876 3.962 7.294

<0.010

25-29 years 1.834 3.332 7.532

30-34 years 0.952 2.758 6.692

35-39 years 0.770 2.044 5.236

40-44 years 0.098 1.064 2.954

45-50 years 0.042 0.224 2.058

11

Women who presented AMH values below the lower limit of quantification (LLQ = 0.010

ng/mL) were included in the group A and were considered as the women in postmenopausal phase.

Women with detectable AMH levels were considered as women in the perimenopausal phase and were

divided in 4 different groups according to their AMH levels. Samples with AMH values higher than the

LLQ and below the 10th percentile of the reference values (<0.042 ng/mL) were included in the group

B. AMH values between the 10th percentile and the median value were considered as group C (0.042-

0.223 ng/mL). AMH values from the median to the 90th percentile (0.224-2.058 ng/mL) were included

in the group D. Finally, AMH values above the 90th percentile (>2.058 ng/mL) were included in the

group E.

2.2 Hormone quantification

All four hormones were quantified, previous to this study, following urine sample collection.

AMH quantification was accomplished by electrochemiluminescence immunoassay (ECLIA)

(Elecsys® AMH assay, Roche Diagnostics) on Cobas 411 with a lower limit quantification (LLQ) of

0.01 ng/mL and LH (2P4035 ARCHITECT Kit - 0.09 mIU/mL), FSH (7K75-35 ARCHITECT Kit -

0.05 mIU/mL) and E2 (7K72-35 KIT, 25 pg/mL) were quantified in serum by chemiluminescent

microparticle immunoassay (CMIA) on ARCHITECTi2000.

2.3 Urine treatment

Urine samples were processed following a protein precipitation method (adapted from

Pacchiarotta et al., 2012).

From each sample, 10 µL were used to create a quality control pool (QC pool) in order to assess

the quality of sample processing, both chromatographic and mass spectrometry analysis and the data’s

ultimate reproducibility and subsequent validation (Dunn, Wilson, Nicholls, & Broadhurst, 2012).

Samples were randomized and the extraction protocol was performed. A volume of 50 µL of

each sample or QC pool was mixed with 150 µL of cold ethanol and incubated on ice for 20 minutes.

The samples were then centrifuged at 3660 g and 4°C for 10 minutes. Subsequently, the supernatant was

evaporated until dryness using an Eppendorf Concentrator plus (Eppendorf, Germany). Reconstitution

of samples was made with 400 µL of 10% of acetonitrile in water.

2.4 Sample acquisition

The samples were injected in four sequences in four different days. Each sequence contained

from 23 to 26 samples. QC pools and waters were injected in the beginning of each sequence and every

four samples.

The UHPLC Elute (Bruker Daltonics, Bremen, Germany) was equipped with a pre-column

(SecurityGuard™ ULTRA Cartridges, UHPLC Polar C18, 2.1mm ID, Phenomenex) and a column

(Kinetex 2.6 µm Polar C18 100 Å, LC Column 100 x 2.1 mm, Phenomenex). The UHPLC mobile phases

were water + 0.1% formic acid v/v (phase A) and methanol + 0.1% formic acid v/v (phase B) at a flow

rate of 400 µL/min. The gradient was as follows: 1 min to 5% phase B, then 5 min to 50% phase B, and

4 min to 100% whereas was held for 8 min at 100% phase B. Subsequently in 1 min, the phase B

decreased to 0% and held at 0% for 6 more min. The volume of injection was 3 µL.

12

The mass spectrometer (impact II, Bruker Daltonics) was operated in positive ionization mode

via electrospray at a voltage of 4500 V, end plate offset of 500, 80 L/min of nebulizer gas (N2) and 40

Bar of dry gas (N2) at 200 °C. The mass spectrometer operated in full scan acquisition mode with a

spectra rate of 1.00 Hz. The m/z range was from 50 to 1000 m/z. Sodium formate 10 mM was injected

before each run to calibrate the mass spectrometer.

2.5 Validation of LC-MS analyses

LC-MS files were visualized and assessed in Data Analysis (Bruker Daltonics).

For the purpose of validating our analysis, seven intense ions were randomly selected through

their chromatograms to cover both their total RT range and m/z range. The correspondent area of the

peak and retention time (RT) of each ion were obtained with the Data Analysis software in all the QC

pools injected in each sequence and both mean and standard deviation were calculated. Subsequently,

the coefficients of variation (CV = standard deviation / mean) were determined.

The CV of the successful sequences ranged from 1,38% to 21,78% (See Annex Table S.1) to

which, according to the proposed value of CV < 20% by Godzien et al. (2015) and Karaman (2017)

were consider satisfactory.

2.6 Data preprocessing

Data was then preprocessed using MZmine (version 2.39) (Pluskal, Castillo, Villar-Briones, &

Orešič, 2010). For this purpose, LC-MS files were exported and converted into mzXML files using

Msconvert by Proteowizard (Chambers et al., 2012).

Data preprocessing consisted on four steps: peak detection (mass detection, chromatogram

builder and deconvolution), retention time correction, peak matching and gap filling (Figure 2.1).

Figure 2.1 Data preprocessing steps.

Peak detection takes three steps which started with mass detection using the following

parameters: retention time from 0.5 to 10 min, mass detector as centroid and noise level of 7000. Next,

the chromatograms were built with the same retention time interval, minimum time span of 0.1,

minimum height of 7000 and m/z tolerance of 0.005 m/z or 15.0 ppm. For the peak deconvolution, the

algorithm used was Wavelets (XCMS) with signal to noise threshold of 10, wavelet scales from 0.2 to

5 and peak duration range from 0.1 to 2; raw data was used as peak integration method and average as

m/z center calculation.

The next step was retention time correction, in which the m/z tolerance was set to 0.005 m/z or

10 ppm, the retention time tolerance, 2 min and minimum standard intensity, 7000. Subsequently peak

alignment was performed at the same m/z tolerance (0.005 m/z or 10 ppm), a retention time and a

retention time after correction of 2 min, 0 RANSAC iterations, a minimum number of points of 45%

and a threshold value of 2.

Peak detectionRetention time

correctionPeak matching Gap filling

13

The last step was the gap filling where an intensity tolerance of 30% was considered, the m/z

tolerance was once again maintained (0.005 m/z or 10 ppm) and the retention time tolerance was set to

1 min.

After this procedure, the resulting table containing 1899 ions was exported as a CSV file. Two

filters were applied to reduce the number of ions. The first filter consisted in excluding ions that

belonged exclusively to water samples thus all ions that presented a mean peak area above 7000 were

excluded (661 ions).

After this first filter, 1238 ions remained to which the second filter was applied excluding all

ions that presented a CV above 30% in the QC pools (849 ions). Thus, the final table contained 389 ions.

Finally, the dataset was normalized to correct for differences in metabolite concentrations due to

discrepancies in the patients’ water intake. Since water works as a solvent, these differences will mirror

the dilution instead of the actual metabolic profile of each sample.

Two possible types of normalization were applied: total area normalization and probabilistic

quotient normalization (PQN) (Karaman, 2017). Both normalizations were performed in R (version

3.5.3). PQN normalization was performed with the function pqn after the installation of the Rcpm

package (Dieterle, Ross, Schlotterbeck, & Senn, 2006).

2.7 Statistical analyses

2.7.1 Statistical analyses of clinical data

Demographic and clinical data were analyzed using SPSS (Version 25) software. Non-

parametric tests were chosen due to small sample size (14 to 23 samples per group of AMH) (Hill &

Lewicki, 2007).

Comparisons between two variables were achieved through Chi-square tests or Fisher’s test and,

for comparisons between three or more variables, Kruskal-Wallis tests with post hoc Dunn tests or

Mann-Whitney U tests were performed. A significance level of 0.05 was defined.

Data were expressed as median and interquartile range.

2.7.2 Statistical analyses of metabolomics data

Multivariate analyses were operated using the software SIMCA (version 14.1).

Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA)

were performed to both normalized datasets (total area normalization and PQN). These datasets were

analyzed after centering and scaling either with unit variance or pareto. Moreover, data log

transformation was also tested. PCA and PLS-DA models were then assessed, taking into account the

number of components created and the fitness parameters (cumulative R2, Q2). Besides, PLS-DA

models were also evaluated through permutation tests and their p-value and F value.

Following PLS-DA, p(corr)/VIP score plots were generated to select the most relevant ions as

those ones with a VIP value over 1 and a p(corr) value between -0,6 and 0,6.

Partial least squares (PLS) regression analyses were also performed to assess relationships

between our data and the quantified hormone levels (AMH, E2, FSH and LH).

14

2.8 Metabolites identification

Potential metabolite identifications were searched on online databases according the m/z value

with an error of 10 ppm (Human Metabolome Database and METLIN). In parallel, chemical formulas

were obtained for each ion of interest using the Smart Formula tool from Data Analysis (Bruker

Daltonics) with a mass error < 10 ppm and mSigma < 20.

Then, for each of the ions of interest, 3 samples where their peaks presented high intensity,

defined as > 1x104, and good chromatographic shape were selected.

MS/MS analyses were performed using the sample with the highest intensity for each ion. Both

auto MS/MS and MRM (multiple reaction monitoring) methods were performed. For compound

identifications, MS/MS experiments were performed using ESI-ultra high resolution UHPLC-QTOF

and ESI-ion trap MS (HCTultra, Bruker Daltonics). The data were acquired in a scan range from m/z 50

to 1000 in MRM and auto MS/MS mode and by using an inclusion list of precursor ions of interest (the

ones relevant after statistical data analysis). Collision energies were the following: m/z 203.1490, 30 V;

m/z 221.0910, 30 V; m/z 233.1130, 32 V; m/z 245.1130, 33 V; m/z 285.0820, 33 V and m/z 487.2130,

35 V. All the m/z values within this interval were fragmented with interpolated values of collision energy.

Following MS/MS analyses, experimental MS/MS data were matched against the online

MS/MS information to verify the potential identifications.

15

3 Results

3.1 Characterization of the population

The population with a total of 74 patients was characterized as follows:

Table 3.1 Characterization of the population in study; groups are defined by AMH values (in ng/mL); hormone values are

shown as median and interquartile range [25th percentile-75th percentile].

Group A B C D

AMH (ng/mL) <0.01 <0.042 0.042-0.223 0.224-2.058

n 23 17 14 20

Race Caucasian

n (%) 12 (52.2%) 5 (29.4%) 7 (50%) 12 (60%)

Hormones

AMH (ng/mL) 0.01

[0.01-0.01]

0.02

[0.01-0.03]

0.11

[0.08-0.21]

0.39

[0.25-0.70]

E2 (pg/mL) 10.0

[10.0-17.0]

69.0

[22.0-223.0]

52.0

[17.5-96.5]

87.0

[30.75-183.0]

FSH (mIU/mL) 52.35

[37.62-74.36]

18.83

[8.64-27.07]

6.77

[1.99-14.32]

6.26

[3.80-7.77]

LH (mIU/mL) 27.25

[20.99-38.44]

20.32

[10.47-22.87]

4.12

[1.5-12.59]

4.05

[2.69-6.85]

ARVs

n (%)

NRTIs

Abacavir 6 (26.1%) 6 (35.3%) 5 (35.7%) 4 (20%)

Emtricitabine 13 (56.5%) 7 (41.2%) 5 (35.7%) 12 (60%)

Lamivudine 6 (26.1%) 5 (29.4%) 5 (35.7%) 2 (10%)

Tenofovir 14 (60.9%) 11 (64.7%) 6 (42.9%) 14 (70%)

NNRTIS Nevirapine 1 (4.4%) 4 (23.5%) 3 (21.4%) 2 (10%)

IPs Atazanavir 5 (21.7%) 4 (23.5%) 2 (14.3%) 3 (15%)

Darunavir 4 (17.4%) 4 (23.5%) 1 (7.1%) 5 (25%)

IIs Raltegravir 2 (8.7%) 2 (11.8%) 0 (0%) 2 (10%)

The number of Caucasian and Non-Caucasian patients was similar among groups (Chi-square,

p = 0.299). For AMH, significant differences were found between A and either B (p < 0.05), C and D

(p < 0.005) with higher levels recorded in the three latter groups. For E2, higher values were also

demonstrated in groups B, C and D and were all statistically significant (B and D: p < 0.005; C: p <

0.05). FSH was figuratively inversed to the AMH plot showing lower levels in groups from B to D (B:

p < 0.05; C and D: p < 0.005). Lastly, for LH, the plot followed the same pattern as FSH: the levels of

the groups B to D were lower than A however statistical differences were only verified between group

A and groups C and D (p < 0.005) (Figure 3.1).

Besides these comparisons, there were also statistically significant differences between group

B and D (p < 0.0005) in the AMH hormone analysis.

16

Figure 3.1 Hormone levels in each AMH group; A) AMH in ng/mL, B) E2 in pg/mL, C) FSH in mIU/mL and D) LH in

mIU/mL; Comparisons were made using the Kruskal-Wallis tests and post-hoc Dunn-Bonferroni tests. Boxplots represent

minimum and maximum values, 25th percentile, 75th percentile and median: blue asterisks indicate significant differences (* p

< 0.05; *** p < 0.0005) in comparison with A group; Black asterisks (*) and black circles (°) represent outlier values.

The differences in the hormone levels among groups follow accordingly to the biological events

in which both AMH and E2 decrease and FSH and LH increase with the menopause onset (Takahashi

& Johnson, 2015).

3.2 Quality of the data

Data quality was assessed with two strategies. First, we visualized all base peak chromatograms

of the samples. Second, we performed PCA to assess the quality of the LC-MS analyses.

For the first strategy, chromatograms were visualized to evaluate any irregularities that could

be present. Unexpectedly, some chromatograms were found to show broad peaks with plateau shaped

tops (Figure 3.2). These signs of saturation were present in 15 samples (20.3%).

And, due to these unexpected behaviors, we decided to exclude those 15 samples from the

analysis.

A B

C D

17

Figure 3.2 Example of a saturated ion present in the LC-MS chromatograms found at approximately 3 min of retention time.

The second strategy consisted in multivariate analysis, specifically PCA, and were performed

with both total area normalization and PQN datasets.

The first PCA model was carried out on all 59 samples and the 22 QC pools. The plot scores for

both approaches (total area normalization and PQN) show that QC pools create a cluster demonstrating

few differences between them (Figures 3.3). This represents good quality in the QC pools.

0 1 2 3 4 5 6 7 8 9 10 Time [min]

0.0

0.5

1.0

1.5

7x10

Intens.

KS1833_10_01_11527.d: BPC +All MS

A

B

Figure 3.3 PCA score plot of A) total area normalized data (R2X = 0.632; Q2 = 0.198) and B) PQN data (R2X = 0.678; Q2

= 0.09) of all samples. Samples are colored according to sample type (sample, dark circles or QC pool, blue circles).

18

Subsequently, we excluded the QC pools and assessed if the different days of acquisition of the

samples constituted any influence in our LC-MS analyses through PCA (Figures 3.4).

Figure 3.4 PCA score plot of A) total area normalized data (R2X = 0.604; Q2 = 0.149) and B) PQN data (R2X = 0.424; Q2 = 0.135) of all samples colored by acquisition day (1st day, blue circles; 2nd day, dark circles; 3rd day, red circles and 4th day,

yellow circles).

Results showed a good dispersion between the samples of each day, with no clusters created,

meaning that the day of acquisition did not influence the analyses.

After assessing these results, we concluded that the multivariate analyses performed to our

normalized dataset through total area demonstrated better results than PQN given their R2 values. For

the samples and QC pools models where QC pools quality was assessed, R2 was 0.632 and 0.678 for

total area normalization and PQN, respectively, did not show great differences however for the models

that assessed the quality regarding the day of acquisition of the samples, the R2 values were 0.604 for

total area normalization and 0.424 for PQN.

For these reasons, further analyses were based on total area normalized data.

A

B

19

3.3. Influence of race, menopause, ARV and hormone levels in the metabolic profile

To find possible influences of our different variables in the metabolic profile, the PCA model

created with our 59 samples (PCA model: R2X = 0.604; Q2 = 0.149) was assessed.

Race seemed to have some influence on the metabolic profile since the distribution of the dots

(samples) seemingly created a pattern where Caucasian race samples are predominantly on the top half

of the plot and Non-Caucasian samples on the bottom half (Figure 3.5).

Menopause status, defined by our groups of AMH levels, did not seem to have a big influence

on the metabolic profile since no patterns were visible (Figure 3.6).

Figure 3.5 PCA plot score colored by race (Caucasian, blue circles; non-Caucasian, dark circles).

20

Figure 3.6 PCA score plot colored by AMH group (A, blue circles; B, dark circles; C, red circles and D, yellow circles).

The influence of ARV drugs was also evaluated. Two analyses were performed, considering the

principal ARV (atazanavir, ATV; darunavir, DRV; raltegravir, RAL; and/or nevirapine, NVP) (Figure

3.7) and the backbone ARV (abacavir, ABC and/or tenofovir, TDF) (Figure 3.8).

Figure 3.7 PCA score plot of samples with identified ART regimens colored by principal ART (atazanavir, blue circles;

atazanavir + raltegravir, dark circles; darunavir, red circles; darunavir + raltegravir, yellow circles; nevirapine, green circles;

raltegravir, purple circles; no ARV data, grey circles).

Figure 3.8 PCA score plot of samples with identified ART regimens colored by backbone ART (abacavir, blue circles;

abacavir + tenofovir, dark circles; tenofovir, red circles; no ARV data, grey circles).

Considering these score plots, the principal ARV and the backbone ARV had no visible patterns

thus showing no influence.

21

Since no clear influence of AMH was observed in the totality of our samples (Figure 3.6), we

decided to perform PCA models in two-group combinations of the predefined AMH groups in order to

find possible patterns of influence. The following combinations were assessed: A and B, A and C, A

and D, B and C, B and D and C and D (Figure 3.9).

Figure 3.9 PCA scores plots of AMH two groups combinations A) A and B (R2X = 0.59; Q2 = 0.121), B) A and C (R2X =

0.524; Q2 = 0.113), C) A and D (R2X = 0.47; Q2 = 0.117), D) B and C (R2X = 0.495; Q2 = 0.049), E) B and D (R2X = 0.538; Q2 = -0.062) and F) C and D (R2X = 0.515; Q2 = 0.005). Samples are colored according to AMH group (A group, blue circles;

B group, dark circles; C, red circles and D, yellow circles).

A B

C D

E F

22

The only resultant PCA model that showed some differences between AMH groups was the one

between groups B and D (PCA model: R2X = 0.538; Q2 = -0.062) due to a pattern that can be

acknowledged: group B tending to the left side of the plot and group D, opposingly, to the right side

(Figure 3.9.E).

Even if the influence of menopause (defined by the AMH levels) on the metabolic profile was

not dominant, we conducted PLS-DA models in order to improve the separation between groups. Two-

class PLS-DA models were performed. The only statistically significant model was B vs D (p < 0.05)

(Figure 3.10).

Figure 3.10 Cross-validated score plot comparing AMH groups B and D. Samples are colored according to AMH group (B

group, dark circles; D group, yellow circles).

The model was created with two components and its statistical parameters were R2X = 0.185,

R2Y = 0.819, Q2 = 0.358, F = 4.21, p value = 0.0092. This PLS-DA result confirms the results obtained

in the analyses through PCA.

In view of these modest results, other types of analysis were tried. PLS models were performed

to evaluate a potential association between the hormonal levels of AMH, E2, FSH and LH and the

metabolic profile (Figure 3.11). However, no model was statistically significant.

23

Figure 3.11 Observed vs predicted plots of each hormone (AMH, E2, FSH and LH); observed values (Y-axis) are correlated

with predicted values produced by the model (X-axis); A) AMH in ng/mL, y = x – 1.074e-8, R2 = 0.647, p = 1; B) E2 in

pg/mL, y = x + 2,759e-6, R2 = 0,6908, p = 1; C) FSH in mIU/mL, y = x - 5,359e-7, R2 = 0,5683, p = 1; LH in mIU/mL, y = x + 1,155e-6, R2 = 0,7064, p = 0.07; plots are colored by AMH group (group A, blue circles; group B, dark circles; group C,

red circles and group D, yellow circles).

So, upon all different analyses performed, we selected the PLS-DA model comparing B and D

as the best model to explain the influence of AMH on the metabolic profile. And, in order to identify

the most important metabolites responsible for the differences between these groups, a p(corr)/VIP score

plot was created (Figure 3.12).

A B

C D

24

3.4 Metabolite identification

For the 34 ions that were selected from the p(corr)/VIP score plot (Figure 3.11) created through

the PLS-DA model which better showed the AMH influence on the metabolic profile, potential

identifications were searched through the m/z values of the ions in online databases. Only 11 ions

showed results (Table 3.2).

Table 3.2 Potential identifications searched in online databases for the ions selected by the p(corr)/VIP score plot; correspondent p(corr) values are expressed as well as the databases which returned results. Shaded ions were selected for

MS/MS analysis.

m/z

[M+H+]

p(corr)

value

Potential identification

Database Chemical formula Name

203.149 0.6046 HMDB/Metlin C8H18N4O2 Symmetric dimethylarginine

Asymmetric dimethylarginine

204.086 0.627 HMDB C8H13NO5 N-acetyl-L-2-aminoadipate (2-)

N-Acetyl-L-2-aminoadipic acid

221.091 0.7225 HMDB/Metlin C11H12N2O3 5-Hydroxy-L-tryptophan

233.113 0.6629 HMDB/Metlin C9H16N2O5

Aspartyl-Valine

Valyl-Aspartate

Threoninyl-Hydroxyproline

241.155 0.6102 HMDB C12H20N2O3 Pirbuterol

245.113 0.6091 HMDB/Metlin C10H16N2O5 Prolyl-Glutamate

285.082 0.6229 HMDB C10H12N4O6 Xanthosine

Diazepam

346.123 0.6785 HMDB C17H19N3O3S Omeprazole

368.153 0.6475 HMDB C21H22ClN3O Desmethylazelastine

487.213 0.605 HMDB C18H34N2O13 Glucosylgalactosyl hydroxylysine

514.876 -0.7904 HMDB C12H10Ca2FeO14 Ferrous calcium citrate

Considering the shape of the peaks, intensity and possible biological interest to our study, six

ions (shaded in Table 3.2) were selected for MS/MS analysis. Through online databases, spectra

Figure 3.12 p(corr)/VIP score plot; Red

circles correspond to the selected ions (VIP

value over 1 and p(corr) value below -0.6

and over 0.6).

25

documented for each ion was searched to find matches to the spectra resultant form MS/MS analysis.

However, no matches were found for any of the 6 ions.

3.5 Analysis of saturation in samples

As already mentioned, from 74 samples of this study, 15 presented saturated ions in forms of

plateau.

We performed a deeper analysis of saturation effects in the 74 samples and defined saturation

as either an intensity of 1x107 or the presence of split peaks (peaks that show the same m/z and extremely

approximate retention time; Annex Figure S.1). After assessment of all samples, and considering these

new criteria, a total of 56 samples showed saturated ions (75.7%).

A list with 28 ions was retrieved from the saturated peaks. Identifications were performed

against online metabolomic databases and the results of these searches concluded that 14 ions (50%)

could represent ARV metabolites, 6 ions (21.4%), other compounds’ metabolites and the remaining 8

ions (28.6%) were inconclusive since no matches were found (Table 3.3).

Table 3.3 Potential identifications for the saturated ions (characterized by a minimum intensity of 1x107).

m/z [M+H+] Chemical formula Name

180.0657 C9H9NO3 3-Succinoylpyridine

230.0594 C8H11N3O3S Lamivudine

248.0499 C8H10FN3O3S Emtricitabine

265.1189 C13H16N2O4 Phenylacetylglutamine

287.1601 C14H18N6O Abacavir

288.0856 C9H14N5O4P Tenofovir

290.1395 C16H19NO4 Benzoyl ecgonine

296.0701 C12H13N3O4S N4-Acetylsulfamethoxazole

301.1415 C14H16N6O2 5′-carboxylic acid abacavir

316.0346 C14H9ClF3NO2 Efavirenz

332.1414 C17H18FN3O3 Ciprofloxacin

339.0622 C16H15ClO6 6-Demethylgriseofulvin

392.2001 C20H29N3O3S Darunavir metabolite M19

435.0563 C20H15BrN6O Etravirine

445.1627 C20H21FN6O5 Raltegravir

459.1509 C21H22N4O8

2-hydroxynevirapine glucuronide

3-hydroxynevirapine glucuronide

8-hydroxynevirapine glucuronide

12-hydroxynevirapine glucuronide

463.1948 C20H26N6O7 5′-glucuronide abacavir

548.2429 C27H37N3O7S Darunavir

705.3982 C38H52N6O7 Atazanavir

721.3200 C37H48N6O5S2 Ritonavir

26

We proceeded to the confirmation of all the potential identifications through MS/MS analysis.

Out of the 20 ions that positively got matches on online databases, 17 were confirmed through the

MS/MS analysis (Figure 3.13). Some fragments demonstrated high errors (> 10 ppm) mostly due the

low intensity of the MS/MS spectra (Petyuk et al., 2008).

H+

[M+H]+

m/z 180.0655

(± 7.77 ppm)

C9H9NO3

m/z 134.0600

(± 3.73 ppm)

C8H8NO+

m/z 162.0549

(± 3.09 ppm)

C9H8NO2+

N

O

O

OH

A

H+

[M+H]+

m/z 230.0594

(± 0.86 ppm)

C8H11N3O3S

m/z 112.0505

(± 66.93)

C4H6N3O+

B

H+

[M+H]+

m/z 248.0499

(± 4.84 ppm)

C8H10FN3O3S

[M+H]+

m/z 130.0411

(± 28.45 ppm)

C4H4FN3O

C

S

OOH

O

N

N

NH2

OH NH

N

NH2

+

H+

F

O NH

N

NH2

S

F

O

OH

O

N

N NH2

N

OH+

N

O

O+

27

H+

[M+H]+

m/z 265.1183

(± 2.64 ppm)

C13H16N2O4

m/z 84.0444

(± 7.14 ppm)

C4H6NO+

m/z 147.0764

(± 1.36 ppm)

C5H10N2O3

D

NH

O

NH2

O

OHO

NH2

O+

CH2

+

m/z 91.0542

(± 1.10 ppm)

C7H7+

NH2NH2

O H+

[M+H]+

m/z 101.0709

(± 0 ppm)

C4H8N2O

NH2

O+

OHO

m/z 130.0499

(± 0.77 ppm)

C5H8NO3+

NH2

O

[M+H]+

m/z 136.0757

(± 0 ppm)

C8H9NO

NH2NH2

O

OHO

H+

H+

H+

[M+H]+

m/z 287.1615

(± 4.88 ppm)

C14H18N6O

m/z 134.0461

(± 2.24 ppm)

C5H4N5+

m/z 191.1040

(± 3.14 ppm)

C8H10N6

E

N

NH

NH2

N

[M+H]+

m/z 109.0509

(± 1.83 ppm)

C4H4N4

H+

N

NH

N+

NH2

N

m/z 174.0774

(± 0 ppm)

C8H8N5+

H+

N

N

N

NH

N

NH2

OH

N

NH

N+

NH

N

N

NH

N

NH

N

NH2

28

H+

[M+H]+

m/z 288.0856

(± 14.23 ppm)

C9H14N5O4P

[M+H]+

m/z 136.0618

(± 3.67 ppm)

C5H5N5

m/z 270.0751

(± 8.89 ppm)

C9H12N5O3P

F

m/z 290.1387

(± 7.24 ppm)

C16H20NO4+

m/z 168.1019

(± 12.49 ppm)

C9H14NO2+

m/z 272.1281

(± 11.02 ppm)

C16H18NO3+

G

N

O

OH2

+

OO

N

OH

OH2

+

N

OO

OH+

P

O

O

OH

N

N

N

N

NH2

OH

NH

N

N

N

NH2

N

N

N

N

NH2

+

m/z 176.0931

(± 1.70 ppm)

C8H10N5+

O

N

N

N

N

NH2

+

m/z 206.1036

(± 3.40 ppm)

C9H12N5O+

P

O

OO

N

N

N

N

NH2

H+

29

[M+H]+

m/z 296.0699

(± 5.07 ppm)

C12H13N3O4S

[M+H]+

m/z 92.0495

(± 7.60 ppm)

C6H6N+

m/z 198.02194

(± 4.54 ppm)

C8H8NO3S+

H

N

N

NH

OHO

O

O

S

H+

NH2

H+

N

N

N

N

OH

O

NH2

NHH

+

[M+H]+

m/z 301.1408

(± 1.33 ppm)

C14H16N6O2

[M+H]+

m/z 109.0509

(± 0 ppm)

C4H4N4

m/z 174.0774

(± 0.57 ppm)

C8H8N5+

I

N

NH

NH2

N

H+

N

NH

N+

NH2

N

m/z 162.0549

(± 3.09 ppm)

C9H8NO2+

N

NH

N+

NH

N

N

NH

N

NH

N

NH2

m/z 191.1040

(± 0.52 ppm)

C8H10N6

H+

S

N

OH

O+

O

30

[M+H]+

m/z 332.1405

(± 2.11 ppm)

C17H18FN3O3

[M+H]+

m/z 245.0921

(± 59.16 ppm)

C13H12N2O3

m/z 314.1299

(± 2.23 ppm)

C17H17FN3O2+

J

[M+H]+

m/z 339.0630

(± 5.90 ppm)

C16H15ClO6

m/z 69.0335

(± 0 ppm)

C4H5O+

m/z 271.0368

(± 4.80 ppm)

C12H12ClO5+

K

F

O

OH

O N

N NH

[M+H]+

m/z 288.1343

(± 51.71 ppm)

C15H17N3O3

O

OH

O N

NH2

O

OH

O N

NH NH2

F

O

O+

N

N NH

H+

H+

H+

Cl

O

OH

O O

O

O

H+

OH2

+

OH

OH+

OCl

OH

O

O

OH+

Cl

O

O

O

OH+

O

Cl

OH

O

O

O

O

m/z 165.0546

(± 6.06 ppm)

C9H9O3+

m/z 200.9949

(± 3.98 ppm)

C8H6ClO4+

[M+H]+

m/z 307.0367

(± 4.23 ppm)

C15H11ClO5

H+

31

m/z 392.2002

(± 3.57 ppm)

C20H30N3O3S+

m/z 156.0114

(± 10.90 ppm)

C6H6NO2S+

m/z 241.1005

(± 11.20 ppm)

C11H17N2O2S+

L

[M+H]+

m/z 445.1630

(± 21.57 ppm)

C20H21FN6O5

m/z 253.0931

(± 14.22 ppm)

C10H13N4O4+

[M+H]+

m/z 361.1307

(± 2.49 ppm)

C17H17FN4O4

M

S

OH

O

O

NH3

+

N

NH2

S

O

O

N+

NH2

O

NN

N

NF

NH

O

OH

O

NH

O

N

NF

N

O

OH

O

NH

O

253 .0967

H+

H+

S

NH2

O+

O

N

N

NH2

O

OH

O

NH

O+

32

[M+H]+

m/z 459.1510

(± 4.79 ppm)

C21H22N4O8

m/z 242.0793

(± 3.72 ppm)

C12H9N4O2+

m/z 283.1189

(± 1.77 ppm)

C15H15N4O2+

N

[M+H]+

m/z 463.1936

(± 3.45 ppm)

C20H26N6O7

[M+H]+

m/z 109.0509

(± 3.67 ppm)

C4H4N4

m/z 174.0774

(± 1.72 ppm)

C8H8N5+

O

N

NH

NH2

N

H+

N

NH

N+

NH2

N

m/z 162.0549

(± 1.49 ppm)

C9H8NO2+

N

NH

N+

NH

N

N

NH

N

NH

N

NH2

m/z 191.1040

(± 1.05 ppm)

C8H10N6

H+

N

N

N

NH

N

NH2

OH

H+

N

N

N

N

NH

NH2O

O

OH

O

OH

OH OH

H+

[M+H]+

m/z 287.1615

(± 1.74 ppm)

C14H18N6O

NN

N

N

OH

OH

OH

OHO

OH

O

O

H+

NN

N

NH

O

OH2

+

NN

N

N

O

OH2

+

33

Figure 3.13 MS/MS spectra of the ions that matched online metabolomic data and correspondent chemical structures of their

fragments; A) ion at m/z 180.0657 (3-Succinoylpyridine), B) ion at m/z 230.0594 (Lamivudine), C) ion at m/z 248.0499

(Emtricitabine), D) ion at m/z 265.1189 (Phenylacetylglutamine), E) ion at m/z 287.1601 (Abacavir), F) ion at m/z 288.0856

(Tenofovir), G) ion at m/z 290.1395 (Benzoyl ecgonine), H) ion at m/z 296.0701 (N4-Acetylsulfamethoxazole), I) ion at m/z 301.1415 (5′-carboxylic acid abacavir), J) ion at m/z 332.1414 (Ciprofloxacin), K) ion at m/z 339.0622 (6-

Demethylgriseofulvin), L) ion at m/z 392.2001 (Darunavir M19), M) ion at m/z 445.163 (Raltegravir), N) ion at m/z 459.151

(2-hydroxynevirapine glucuronide), O) ion at m/z 463.1948 (5′-glucuronide abacavir), P) ion at m/z 548.243 (Darunavir) and

Q) ion at m/z 705.398 (Atazanavir); spectra were obtained through either MRM or auto MS/MS methods. The error of each

ion is shown in parentheses.

[M+H]+

m/z 548.2425

(± 1.09 ppm)

C27H37N3O7S

m/z 392.2002

(± 1.27 ppm)

C20H30N3O3S+

[M+H]+

m/z 436.1901

(± 0.91 ppm)

C21H29N3O5S

P

[M+H]+

m/z 705.3970

(± 2.84 ppm)

C38H52N6O7

m/z 168.0808

(± 4.16 ppm)

C12H10N+

m/z 335.1965

(± 5.67 ppm)

C18H27N2O4+

Q

H+

S

O

O

O

OH

O

O

O

NH

N

NH2

H

H

S

OH

O

O

NH3

+

N

NH2

S

OH

OH

O

O

O

NH

N

NH2 H+

OH

O

O

O

O

O

O

NH

N

NH

NH NH

N H+

N+

OH

O

O

O

NHNH

CH2

+

34

We could then state that out of the 55 samples showing saturated peaks, 39 were saturated with

ARV metabolites and 16 with other metabolites exclusively. We then analyzed if there were differences

in the antiretroviral drugs found in the samples showing saturated peaks (saturated samples) and samples

without saturated peaks (non-saturated samples). A total of 68 samples were considered for these

analyses after exclusion of 6 samples due to the lack of ARV therapy.

Differences between the presence of each ARV drug in saturated and non-saturated samples

were not statistically significant (Fisher’s exact tests: ABC, p = 0.52; ATV, p = 1; DRV, p = 1; NVP, p

= 0.189; RAL, p = 0.322; TDF, p = 1; 3TC, p = 0.733; and Chi-square test for FTC, p = 0.506) (Figure

3.14).

Figure 3.14 Representation of the number of saturated and non-saturated samples taking antiretroviral drugs (ART). Non-

saturated samples are shaded light grey and saturated samples, dark grey.

Regarding differences in the races between saturated samples and non-saturated samples,

analysis showed no statistical significance (Chi-square test, p = 0.054) (Figure 3.15). Mann-Whitney

tests indicated that there were also no differences among values of AMH, E2 and LH between the non-

saturated samples and the saturated samples (AMH, p = 0.096; E2, p = 0.193; LH, p = 0.081). However,

for FSH (p = 0.045), significant differences were shown. FSH levels were higher in the non-saturated

samples (Figure 3.16).

Figure 3.15 Representation of the number of samples by race in both saturated and non-saturated samples; Non-saturated

samples are shaded light grey and saturated samples, dark grey.

35

Figure 3.16 Boxplots of hormone values; Values of A) AMH in ng/mL B) E2 in pg/mL, C) FSH in mIU/mL and D) LH in mIU/mL in saturated and non-saturated samples. Comparisons were made through Mann-Whitney tests. Boxplots represent

minimum and maximum values, 25th percentile, 75th percentile and median: blue asterisk indicates significant differences (* p

< 0.05). Black asterisks (*) and black circles (°) represent outlier values.

A B

C D

36

4 Discussion

Our study aimed to find metabolic differences between HIV non-menopausal women and HIV

menopausal women in order to understand the role of menopause on female metabolism and in what

way it increases the risk of metabolic diseases.

For this purpose, we analyzed 74 urine samples belonging to women of similar chronological

age (between 45 and 49 years old) but with different biological age. The latter was assessed through

their AMH levels from which women were grouped into different levels of perimenopause and

postmenopause. It is important to highlight that women included in this study had similar ages. If this

study had included a larger age span, it would have been impossible to differentiate between the age-

related and menopause-related effects in the metabolic profile.

AMH was used to assess the menopausal status groups of our samples because it is the best

indicator of ovarian aging, in which its values decrease with age until menopause onset where it reaches

undetectable values. Its stability throughout the menstrual cycle and not being influenced by the

presence of oral contraceptives also grants security in the values obtained for women on perimenopause

(Bleil et al., 2013; Dewailly et al., 2016; Oh et al., 2019).

Menopause is described to promote numerous metabolic alterations due to the loss of estrogen.

The most common metabolic alterations affect the vascular integrity reducing permeability to LDLs, the

endothelial cell survival, the regulation of angiogenesis, the inhibition of inflammation and ROS and

the lipid profile (triglycerides, total cholesterol, low-density lipoprotein and apolipoprotein B) (Fortini

et al., 2019). These changes may lead to severe conditions including cardiovascular diseases, namely

myocardial infarction and stroke, renal diseases, and osteoporosis (Auro et al., 2014; Polotsky &

Polotsky, 2010; Wang et al., 2018).

Our first expectation was to find metabolic differences between the groups A and D. Since the

patients in group A are considered postmenopausal and the patients belonging to group D are the furthest

from the menopause onset event and, being supported by the results of the clinical data statistics (p <

0.005 for all four hormones). However, no metabolic differences were identified between these two

groups.

Metabolic differences were found between B and D, which confirms the influence of AMH in

the metabolic profile. These results are strengthened by the post-hoc analysis of our AMH data where

significant differences between groups B and D were described. Moreover, it is described that women

closer to menopause onset (B group) are the ones that have the highest number of metabolic changes

because it is during this stage that women initiate the metabolic alterations that define postmenopause,

specifically proatherogenic lipid changes that include, as already mentioned, higher TC, TG, LDL and

apoB due to the loss of estrogen protective effects (Derby et al., 2009; Polotsky & Polotsky, 2010;

Stachowiak et al., 2015). All these arguments confirm the metabolic differences observed between B

and D groups. Six potential metabolite candidates were found in the metabolic databases from which

symmetric dimethylarginine/asymmetric dimethylarginine was flagged with particular interest due to its

interference in the production of nitric oxide, a chemical of extreme importance to endothelial health

and thus cardiovascular health (Duranton et al., 2012). Glucosylgalactosyl hydroxylysine was also of

interest for participating in the modification of collagen (Szulc, Seeman, & Delmas, 2000).

Unfortunately, MS/MS experiments were not useful to confirm these potential identifications.

37

During the LC-MS analyses of the samples of our study, unexpected saturations were observed

in 74% of the samples to which 50% of the saturated ions corresponded to antiretroviral drugs. This

result confirms the high interindividual variability in the pharmacokinetics of antiretroviral drugs

(Cattaneo et al., 2012; Gervasoni et al., 2016; Loutfy et al., 2013). Hormones can be an important factor

responsible for the interindividual variability in ARVs concentrations. In our study, lower levels of FSH

were characteristic of the saturated samples. However, according to Hale et al. (2014) the reliability of

FSH levels in late perimenopause can be quite low because of its variability throughout the menstrual

cycle. For this reason, the credibility of the differences found in FSH can be put at test. This also weights

on the importance of having registry of the collection day of each sample.

This study presents several limitations that should be pointed out.

The first limitation is related with the methodology used in our study to define postmenopause.

The menopausal status was defined according to the AMH levels and not according to the clinical

definition of menopause (diagnosed after 12 months of amenorrhea). Women in our study were

considered in postmenopause according to an AMH level equal or lower than 0.01 ng/mL which

corresponds to the quantitation limit of the AMH measurement essay used in our samples. Although

AMH correlates with the ovarian reserve (Carrarelli et al., 2014) and thus undetected values would mean

menopause onset had occurred before, miss classifications might have happened. There have been cases

of women presenting higher levels of AMH while in postmenopause as well as lower levels of AMH

while in perimenopause (de Kat et al., 2019; Kyweluk et al., 2018; Antonio La Marca et al., 2005).

Robertson, et al. (2011) have also confirmed changes of AMH levels throughout the ovulatory menstrual

cycle in perimenopause and concluded that AMH at this phase becomes less reliable due to its high

variability. In their study, the authors noticed that, during longer ovulatory cycles (characteristic of

perimenopausal women), AMH levels varied enough to become undetected in their essay (quantification

limit of 0.017 ng/mL). Keeping in mind that our quantification limit was below this value, at 0.01 ng/mL,

this observation still classifies as a possibility that could have occurred with our samples. Thus, a direct

confirmation of the menopausal status with the patient should have been considered. Likewise, data on

women who have regular ovulatory menstrual cycles and women with longer cycles and amenorrhoeic

months was not collected. This information might have hindered a correct assignment of the samples to

each of the reproductive aging stages: premenopause, perimenopause (early or late) and postmenopause.

Furthermore, the lack of registry of the menstrual cycle day in which sample collection at the

hospitals was made implies a possibility of higher or lower hormone levels, specifically FSH, LH and

E2, due to different menstrual phases (Hale et al., 2014; Santoro & Randolph, 2011).

Data such as hormone replacement therapy (HRT) which main goals are to diminish the

menopausal symptoms and the risk of cardiovascular diseases (Auro et al., 2014; Knowlton & Korzick,

2014; Yang & Reckelhoff, 2011); the smoking status that is strongly associated with metabolic disorders

in women (Kwaśniewska et al., 2012; Polotsky & Polotsky, 2010); the use of oral contraception and the

use and abuse of drugs (Schoenbaum et al., 2005); the age of menarche, since it has been showed that

an early (≤ 10 years old) or late menarche (≥ 16 years old) represent a higher risk of CVD (Stachowiak

et al., 2015) and the presence of any comorbidities (obesity, history of diabetes, high blood pressure or

heart disease, among others) (Johnson et al., 2014; Oh et al., 2019) could have been used to correctly

select the exclusion criteria as well as a better-understanding of the AMH values for each patient. These

missing data are of extreme importance because throughout the different menopausal stages, different

metabolic changes occur which trigger various symptoms and conditions such as increased vasomotor

symptoms in late perimenopause (Gold et al., 2006), increased LDL and triglycerides levels between

early perimenopause and early menopause and metabolic syndrome (abdominal obesity, dyslipidemia,

38

diabetes, and hypertension) (Hale et al., 2014) which may be biased due to these sociodemographic and

clinical factors.

Finally, we were not able to identify any of the metabolites associated with the PLS-DA model that

found differences between groups B and D. The quality of the MS/MS spectra was not satisfactory due

to the low intensity of their signals which most likely induced high mass errors and thus did not allow

to compare them with online MS/MS spectra of the potential candidates. There can be several reasons

responsible for the bad quality of the MS/MS spectra:

1. Possible saturation of the samples used for MS/MS, which can trigger a detector overload during

LC-MS analyses. The possible resolution for this issue is the dilution of the samples (Sargent,

2013). However, due to the presence and importance of metabolites with much lower intensity,

diluting the samples would generate a considerable risk of losing important metabolic data from

our samples.

2. The possibility of false positives by overfitting due to the difference in the big number of

variables (n=389) and low number of samples (n=69). Overfitting is very common risk in

metabolomic studies, which usually present a large number of metabolites and a relatively small

sample size. Overfitted model scan include irrelevant noise although statistically significant

metabolites with no biological interest to the goal of the study (Bartel, Krumsiek, & Theis, 2013;

Kelly et al., 2018; Xi, Gu, Baniasadi, & Raftery, 2014).

4.1 Conclusions

This study verified metabolic differences between women close to menopause that although

having similar chronological ages, manifested different biological ages according to their AMH levels.

To the best of our knowledge, this is the first study where menopause was assessed in HIV-infected

women through a non-targeted metabolomics approach.

Taking into consideration the increased risks of metabolic related diseases that HIV-infected

women going through menopausal transition into menopause face, it is of extreme importance to identify

early biomarkers of reproductive ageing in order to promote strategies to minimize health detriment to

these women. For that reason, further studies on menopause in HIV-infected women are of utmost

importance.

39

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Annexes

Table S.1 Validation of LC-MS analyses: peak area coefficients of variation (CV); RT, retention time; SD, standard deviation.

Sequence 1 Sequence 2 Sequence 3 Sequence 4

m/z RT (min) Mean SD CV (%) Mean SD CV (%) Mean SD CV (%) Mean SD CV (%)

1 242.925 0.7 3354005 46165 1.38 2480550 411167 16.58 3277789 108944 3.32 3489131 75807 2.17

2 463.194 1.0 49950204 9790167 19.60 123166258 4698304 3.82 98374835 12896970 13.11 120250571 5446149 4.53

3 301.141 3.2 176471946 13696766 7.76 142873126 4471536 3.13 158611619 3137536 1.98 138456512 3153594 2.28

4 265.119 3.5 66067998 9974023 15.10 61382454 3100712 5.05 52118752 931706 1.79 63219550 3435275 5.43

5 748.485 6.0 18473474 3090638 16.73 23147831 4256229 18.39 15780217 402052 2.55 17914276 1065084 5.95

6 705.397 6.9 9513423 2071801 21.78 11874612 1930102 16.25 9421589 369320 3.92 10872715 709911 6.53

7 289.154 8.5 299663 58830 19.63 756895 84913 11.22 151121 6070 4.02 848701 55288 6.51

Figure S.1 Chromatographic representation of double peaks at approximately 3.8 min of retention time.

0 1 2 3 4 5 6 7 8 9 10 Time [min]

0.0

0.2

0.4

0.6

0.8

1.0

7x10

Intens.

KS3777_16_01_11750.d: BPC +All MS