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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Nutr.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Nutrition</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Nutr.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2296-861X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnut.2025.1731915</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Ketosis suppression and ageing (KetoSAge): the effect of suppressing ketosis on SHBG and sex hormone profiles in healthy premenopausal women, and its implications for cancer risk and therapy</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes"><name><surname>Cooper</surname> <given-names>Isabella D.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref><xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author"><name><surname>Petagine</surname> <given-names>Lucy</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author"><name><surname>Soto-Mota</surname> <given-names>Adrian</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref><xref ref-type="aff" rid="aff4"><sup>4</sup></xref><xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author"><name><surname>Lee</surname> <given-names>Derek C.</given-names></name><xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
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<contrib contrib-type="author"><name><surname>Kyriakidou</surname> <given-names>Yvoni</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="author-notes" rid="fn0001"><sup>&#x2020;</sup></xref>
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<aff id="aff1"><label>1</label><institution>Metabolic Endocrine Cancer Cardiovascular and Ageing Research, Centre for Nutraceuticals, School of Life Sciences, University of Westminster</institution>, <city>London</city>, <country country="gb">United Kingdom</country></aff>
<aff id="aff2"><label>2</label><institution>Neuroscience, Inflammatory Disorders and Therapeutics Group, Research Centre for Nutraceuticals, School of Life Sciences, University of Westminster</institution>, <city>London</city>, <country country="gb">United Kingdom</country></aff>
<aff id="aff3"><label>3</label><institution>Unidad de Investigaci&#x00F3;n en Enfermedades Metab&#x00F3;licas, Instituto Nacional de Ciencias M&#x00E9;dicas y Nutrici&#x00F3;n Salvador Zubir&#x00E1;n</institution>, <city>Mexico City</city>, <country country="mx">Mexico</country></aff>
<aff id="aff4"><label>4</label><institution>Tecnologico de Monterrey, School of Medicine</institution>, <city>Mexico City</city>, <country country="mx">Mexico</country></aff>
<aff id="aff5"><label>5</label><institution>Biology Department, Boston College</institution>, <city>Chestnut Hill</city>, <state>MA</state>, <country country="us">United States</country></aff>
<aff id="aff6"><label>6</label><institution>Faculty of Science and Engineering, Queen Mary University of London</institution>, <city>London</city>, <country country="gb">United Kingdom</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Isabella D. Cooper, <email xlink:href="mailto:i.cooper@westminster.ac.uk">isabella@bellamitochondria.com</email></corresp>
<fn fn-type="other" id="fn0001">
<label>&#x2020;</label>
<p>ORCID: Isabella D. Cooper, <uri xlink:href="https://orcid.org/0000-0001-7374-4340">orcid.org/0000-0001-7374-4340</uri>; Lucy Petagine, <uri xlink:href="https://orcid.org/0000-0003-2457-4001">orcid.org/0000-0003-2457-4001</uri>; Adrian Soto-Mota, <uri xlink:href="https://orcid.org/0000-0002-9173-7440">orcid.org/0000-0002-9173-7440</uri>; Tom&#x00E1;s Duraj, <uri xlink:href="https://orcid.org/0000-0002-7778-0194">orcid.org/0000-0002-7778-0194</uri>; Thomas N. Seyfried, <uri xlink:href="https://orcid.org/0000-0003-1491-3989">orcid.org/0000-0003-1491-3989</uri>; Yvoni Kyriakidou, <uri xlink:href="https://orcid.org/0000-0002-8883-2228">orcid.org/0000-0002-8883-2228</uri></p>
</fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-01-02">
<day>02</day>
<month>01</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2025</year>
</pub-date>
<volume>12</volume>
<elocation-id>1731915</elocation-id>
<history>
<date date-type="received">
<day>24</day>
<month>10</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>19</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>27</day>
<month>11</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2026 Cooper, Petagine, Soto-Mota, Duraj, Seyfried, Lee, Cooper and Kyriakidou.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Cooper, Petagine, Soto-Mota, Duraj, Seyfried, Lee, Cooper and Kyriakidou</copyright-holder>
<license>
<ali:license_ref start_date="2026-01-02">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec id="sec1001">
<title>Introduction</title>
<p>Insulin resistance and hyperinsulinaemia significantly influence female hormone regulation and reproductive health. Despite increasing research, the complex pathways by which nutritional and metabolic signals regulate reproductive function remain poorly understood. Sex hormone-binding globulin (SHBG) is a key protein whose function is modulated by hyperinsulinaemia, liver function, and metabolic status, thereby influencing the active signalling of circulating sex steroids and intracellular signalling, which in turn, impacts endocrine and reproductive physiology. Consequently, SHBG serves as a valuable biomarker for understanding the metabolic-hormonal interactions within the endocrine axis. Ketogenic diets have demonstrated efficacy in reversing insulin resistance, resolving markers of liver disease, and improving metabolic health. In this study, we investigated the impact of suppressing ketosis (hypoketonaemia) on biomarkers of female reproductive and endocrine function in the Ketosis Suppression and Ageing cohort.</p>
</sec>
<sec id="sec2001">
<title>Methods</title>
<p>Ten lean (BMI, 20.52 kg/m<sup>2</sup>&#x202F;&#x00B1; 1.39), healthy, premenopausal women (mean age, 32.30 &#x00B1; 8.97 years), who maintained nutritional ketosis for an average of 3.9 years (&#x00B1; 2.3), participated in a three-phase intervention trial: 21-days of baseline data-collection in euketonaemia, 21-days of hypoketonaemia, and 21-days return to euketonaemia.</p>
</sec>
<sec id="sec3001">
<title>Results</title>
<p>Suppression of ketosis resulted in a significant 0.67-fold decrease in SHBG levels (<italic>p</italic>&#x202F;=&#x202F;0.0015). SHBG was significantly and inversely associated with insulin (<italic>p</italic>&#x202F;=&#x202F;0.0010), insulin resistance score (HOMA-IR; <italic>p</italic>&#x202F;=&#x202F;0.0012), glucose ketone index (GKI; <italic>p</italic>&#x202F;=&#x202F;0.0183), leptin (<italic>p</italic>&#x202F;=&#x202F;0.0016), insulin-like growth factor-1 (IGF-1; <italic>p</italic>&#x202F;=&#x202F;0.0172), free T3 (<italic>p</italic>&#x202F;=&#x202F;0.0001), and gamma-glutamyl transferase (GGT; <italic>p</italic>&#x202F;=&#x202F;0.0024). A significant positive association between SHBG and GLP-1 (<italic>p</italic>&#x202F;=&#x202F;0.0295) was observed. Menstrual cycle phase was a statistically significant predictor of follicle-stimulating hormone (FSH) levels, with higher FSH levels during ovulation than during the follicular phase (<italic>p</italic>&#x202F;=&#x202F;0.0097).</p>
</sec>
<sec id="sec4000">
<title>Discussion</title>
<p>SHBG is a sensitive biomarker of metabolic-endocrine status, with broader implications for cancer, and reproductive function. Chronic hypoketonaemia negatively affects SHBG production and hormonal balance. The implications of sex-hormone regulation for cancer prevention and therapy are discussed.</p>
</sec>
</abstract>
<kwd-group>
<kwd>ageing</kwd>
<kwd>BHB</kwd>
<kwd>GKI</kwd>
<kwd>HOMA-IR</kwd>
<kwd>hyperinsulinaemia</kwd>
<kwd>insulin resistance</kwd>
<kwd>ketosis</kwd>
<kwd>oestrogen</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declare that no financial support was received for the research and/or publication of this article.</funding-statement>
</funding-group>
<counts>
<fig-count count="8"/>
<table-count count="9"/>
<equation-count count="0"/>
<ref-count count="136"/>
<page-count count="24"/>
<word-count count="15924"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Nutrition and Metabolism</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>In recent years, there has been growing interest in the interplay between metabolic dysfunction and female-specific health conditions, including hormone-related cancers and polycystic ovary syndrome (PCOS). PCOS is a prevalent endocrine disorder affecting women of reproductive age and is frequently associated with metabolic disturbances, including obesity, insulin resistance (IR), and elevated androgen levels (hyperandrogenism) (<xref ref-type="bibr" rid="ref1 ref2 ref3">1&#x2013;3</xref>). Chronic hyperinsulinaemia, a hallmark of modern lifestyle-related metabolic factors, has been identified as a central driver in the development of oestrogen-dominant pathologies, such as breast, endometrial, and ovarian cancers, as well as PCOS (<xref ref-type="bibr" rid="ref4 ref5 ref6 ref7 ref8 ref9 ref10 ref11 ref12 ref13 ref14 ref15">4&#x2013;15</xref>). These conditions often coexist with features of metabolic syndrome (MetS), highlighting the intricate link between reproductive and metabolic health in women. Chronically elevated basal levels of insulin, i.e., hyperinsulinaemia, suppresses ketogenesis, which is detectable as <italic>hypoketonaemia</italic>, where beta-hydroxybutyrate (BHB) ketone body concentrations are for several consecutive days consistently &#x003C; 0.5&#x202F;mmol/L before the evening meal at least three hours post-prandially (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref17">17</xref>).</p>
<p>Sex hormones, particularly oestrogens, androgens, and progesterone, play a significant role in regulating insulin sensitivity and metabolic function, including processes related to energy balance and inflammatory pathways (<xref ref-type="bibr" rid="ref18">18</xref>). Oestrogen exerts protective effects on metabolic regulation, especially in glucose homeostasis and insulin sensitivity, and modulates immune cell activation and function through multiple mechanisms. Reduced oestrogen levels, commonly observed during menopause and in certain hormonal disorders, have been associated with an increased risk for type 2 diabetes (T2DM) and cardiovascular disease (CVD).</p>
<p>Oestrogen and testosterone circulate in the bloodstream primarily bound to carrier proteins, most notably the liver-synthesised glycoprotein sex hormone-binding globulin (SHBG) and albumin. SHBG is a key regulator of the bioavailability of circulating sex steroids, and its serum concentrations modulated by both hormonal and metabolic factors, especially insulin (<xref ref-type="bibr" rid="ref1">1</xref>, <xref ref-type="bibr" rid="ref19">19</xref>). Emerging evidence suggests that the balance between testosterone and oestrogen may be associated with carbohydrate metabolism and insulin (<xref ref-type="bibr" rid="ref20">20</xref>). Within this context, lower circulating SHBG concentrations are often considered indicative of a more androgenic hormonal environment. It has been proposed that observed associations between SHBG and glycaemic markers (i.e., insulin, fasting glucose or HOMA-IR) may reflect the underlying steroid milieu, rather than a direct metabolic function of SHBG itself (<xref ref-type="bibr" rid="ref21">21</xref>). Additionally, decreased SHBG levels have been linked to an increased risk of developing MetS (<xref ref-type="bibr" rid="ref22">22</xref>), and show inverse correlations with body mass index (BMI) and waist circumference (<xref ref-type="bibr" rid="ref1">1</xref>). Supporting these findings, cross-sectional studies in both men and women have demonstrated that higher SHBG levels correlate with a lower risk of T2DM, with robust associations observed in women (<xref ref-type="bibr" rid="ref20">20</xref>). Lower levels of SHBG are also associated with increased incidence of cancer (<xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref23">23</xref>, <xref ref-type="bibr" rid="ref24">24</xref>). Despite its critical regulatory function, SHBG is rarely measured in clinical practice, even though it plays an essential role in the modulation of sex hormone bioavailability and intracellular signalling modulation. Many cancers, including breast, endometrial, ovarian, prostate, pancreatic, lung and brain tumours are classified as sex hormone-sensitive malignancies (<xref ref-type="bibr" rid="ref13 ref14 ref15">13&#x2013;15</xref>, <xref ref-type="bibr" rid="ref25 ref26 ref27 ref28">25&#x2013;28</xref>). Alterations in SHBG concentrations can therefore affect not only the circulating levels of active sex steroids but also their signalling activity within target tissues, influencing cancer susceptibility and progression.</p>
<p>Recent clinical studies have also highlighted the impact of dietary carbohydrate restriction, particularly ketogenic diets, on SHBG concentrations and metabolic function in women. In both PCOS and non-PCOS populations, <italic>euketonaemia</italic> (BHB&#x202F;&#x2265;&#x202F;0.5&#x202F;mmol/L -&#x202F;&#x003C;&#x202F;5&#x202F;mmol/L) interventions have been associated with increased SHBG levels, alongside improvements in insulin sensitivity, glycaemic control, and androgen profiles (<xref ref-type="bibr" rid="ref16 ref17 ref18">16&#x2013;18</xref>, <xref ref-type="bibr" rid="ref29 ref30 ref31 ref32 ref33 ref34 ref35">29&#x2013;35</xref>). These effects are mediated by reductions in serum basal insulin, whereas higher levels of insulin that suppress ketogenesis causing hypoketonaemia, and concomitantly inhibit hepatic SHBG synthesis (<xref ref-type="bibr" rid="ref36">36</xref>). Consequently, SHBG serves as a sensitive biomarker of metabolic-endocrine status, further refining clinicians&#x2019; and research scientists&#x2019; ability to detect subclinical hyperinsulinaemia (SCHI) and, therefore, the associated increased risk for certain cancers, cardiovascular and neurodegenerative diseases. Earlier detection of SCHI would enable earlier intervention, preventing the development of a serious health condition.</p>
<p>There are limitations with the use of fasting glycaemic and insulin markers. Hyperglycaemia may remain undetected when relying solely on haemoglobin A1c (HbA1c) or fasting glucose measurements, as compensatory hyperinsulinaemia suppresses fasting glucose and masks underlying pathology. In such cases, persistent insulin signalling drives glucose clearance into tissues at an accelerated rate, producing deceptively normal glucose values while concealing chronic metabolic dysfunction (<xref ref-type="bibr" rid="ref37">37</xref>). This concealed state of hyperglycaemia is compounded by the inflammatory and mitogenic effects of sustained hyperinsulinaemia, which amplify systemic risk despite apparently reassuring glycaemic markers (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref38 ref39 ref40 ref41">38&#x2013;41</xref>).</p>
<p>To overcome these diagnostic blind spots, alternative strategies are required. A more sensitive approach involves sequential monitoring of capillary glucose and ketone BHB over a minimum of seven consecutive evenings, either pre-dinner or at bedtime, and at least three hours post-prandially (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>, <xref ref-type="bibr" rid="ref42">42</xref>). This method directly assesses insulin&#x2019;s suppressive effect on hepatic ketogenesis, providing an early marker of metabolic imbalance. In parallel, fasting venous blood biochemistry should incorporate markers that capture the endocrine, inflammatory, and vascular consequences of insulin excess, including plasminogen activator inhibitor-1, gamma-glutamyl transferase (GGT), leptin, homeostasis model assessment for insulin resistance (HOMA-IR), insulin-like growth factor-1 (IGF-1), vascular endothelial growth factor (VEGF), epidermal growth factor, and monocyte chemoattractant protein-1. Together, these indices provide a more comprehensive profile of the metabolic and vascular stress imposed by hyperinsulinaemia (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>).</p>
<p>Conventional standards of care (SOC) define normal glycaemia through fasting glucose and HbA1c values. However, reliance on these static markers fails to capture the metabolic consequences of persistent insulin excess (<xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref37">37</xref>, <xref ref-type="bibr" rid="ref43">43</xref>). When laboratory results indicate glycaemia within the accepted reference interval yet clinical features such as atherosclerosis, or sleep apnoea are present, SCHI should be considered as a primary aetiological driver (<xref ref-type="bibr" rid="ref44 ref45 ref46 ref47 ref48 ref49 ref50 ref51 ref52 ref53 ref54">44&#x2013;54</xref>). These conditions share a unifying metabolic pattern in which euglycaemia is maintained at the cost of chronically elevated insulin, accompanied by suppressed ketogenesis (<xref ref-type="bibr" rid="ref37">37</xref>, <xref ref-type="bibr" rid="ref53 ref54 ref55">53&#x2013;55</xref>). Failure to recognise this compensatory glucose shunting leads to delayed diagnosis and intervention. The elevation of basal insulin, even within reference intervals, not only accelerates glucose uptake into cells but also produces false-negative results in standard clinical tests, thereby obscuring the detection of subclinical hyperglycaemia and early hyperinsulinaemia. This under-recognition allows metabolic damage to accumulate, such as chronic inflammatory signalling, impaired redox regulation, and endothelial dysfunction progress unchecked. Timely detection requires methods that assess both glucose and insulin dynamics, rather than static glucose endpoints to prevent misclassification and intervene before irreversible pathology develops.</p>
<p>This triad of euglycaemia, SCHI, and hypoketonaemia reflects a state of compensated metabolic dysregulation, in which chronic excess insulin exposure supresses the normal production of ketone bodies despite apparently stable glucose concentrations. Hypoketonaemia, therefore, provides a sensitive indicator of hidden metabolic imbalance, signifying insulin-mediated suppression of hepatic ketogenesis that may coexist with euglycaemia or overt hyperglycaemia. Recognition of this metabolic signature is critical, as the absence of abnormal fasting glucose or HbA1c does not equate to metabolic health when hyperinsulinaemia remains unaddressed.</p>
<p>Hypo-ketonaemia and <italic>insulin-compensated euglycaemia</italic> (ICE) define a hidden state of insulin and glycaemic pathology direction that is invisible to conventional standards. Insulin concentrations may fall within population reference intervals, but are elevated relative to individual metabolic capacity, thereby masking subclinical hyperglycaemia (fasting glucose &#x2264; 5.7&#x202F;mmol/L due to insulin-compensation) and subclinical hyperinsulinaemia (&#x2265; 8 &#x03BC;IU/mL). Hypoketonaemia-ICE is a metabolic state of hidden hyperglycaemia, in which glucose is cleared from the bloodstream, yet the total glycaemic load to the bloodstream is not captured. Where within reference range euglycaemia (fasting glucose &#x2264; 5.5&#x202F;mmol/L) and HbA1c are maintained by compensatory insulin activity (hyperinsulinaemia) and revealed by chronic suppression of BHB (hypoketonaemia, &#x003C; 0.5&#x202F;mmol/L) most reliably detected through sequential daily evening capillary blood BHB measurement, where chronic hypoketonaemia demonstrates that basal insulin remains pathologically raised (<xref ref-type="bibr" rid="ref17">17</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref37">37</xref>, <xref ref-type="bibr" rid="ref38">38</xref>, <xref ref-type="bibr" rid="ref40">40</xref>, <xref ref-type="bibr" rid="ref43">43</xref>). A <italic>personalised hyperinsulinaemia threshold</italic> (PIT) is defined by chronic hypoketonaemia as a marker of insulin excess, which is individual-specific and not reliant on population reference ranges. Hypoketonaemia confirms SCHI even when glucose and serum insulin appear normal within standard reference ranges [37,38,43]. Hypoketonaemia-ICE captures the discordant state in which glycaemic indices appear unremarkable, yet underlying insulin-driven compensation reveals early metabolic disturbances.</p>
<p>A distinct metabolic state exists in which fasting glucose and HbA1c remain within conventional reference intervals, yet underlying hyperglycaemia is concealed by compensatory hyperinsulinaemia. In this condition, insulin levels may also appear &#x2018;normal&#x2019; when interpreted against population-based ranges, but are pathologically elevated for the individual, driving excess glucose disposal and preventing hepatic ketogenesis. The most reliable indicator of this state is chronic evening hypoketonaemia, due to persistent insulin suppression of hepatic ketogenesis, defined as persistently low BHB (&#x003C; 0.5&#x202F;mmol/L) despite adequate fasting intervals (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref40 ref41 ref42">40&#x2013;42</xref>). This pattern reveals that basal insulin is set too high for the individual&#x2019;s metabolic capacity, producing an artefactual appearance of normal glycaemia while masking early metabolic dysregulation or disease.</p>
<p>Insulin-Compensated Euglycaemia (ICE):</p>
<list list-type="bullet">
<list-item>
<p>Euglycaemia (normal fasting glucose and normal HbA1c) is achieved through insulin-compensation, not proper metabolic health.</p>
</list-item>
<list-item>
<p>Detects &#x201C;prediabetes,&#x201D; which is stage 1 diabetes (<xref ref-type="bibr" rid="ref38">38</xref>).</p>
</list-item>
<list-item>
<p>Compensated by insulin, revealed by the clinical signature: hypoketonaemia.</p>
</list-item>
</list>
<p>Moving from population-based thresholds to individual metabolic capacity as the diagnostic anchor, the personalised hyperinsulinaemia threshold (PIT) refers to the detection of insulin excess relative to an individual&#x2019;s metabolic capacity, rather than against population-derived reference intervals (<xref ref-type="table" rid="tab1">Table 1</xref>). PIT is identified through the hypoketonaemia-ICE framework, where chronic evening hypoketonaemia (minimum 3&#x202F;h post-prandial BHB&#x202F;&#x003C;&#x202F;0.5&#x202F;mmol/L) reveals that basal insulin levels are elevated, even when fasting insulin remains within conventional laboratory ranges (<xref ref-type="bibr" rid="ref17">17</xref>, <xref ref-type="bibr" rid="ref35">35</xref>). This approach recognises that insulin-induced mitochondrial, metabolic and endocrine dysregulation can occur below standard thresholds and provides a more sensitive and specific means of identifying early metabolic disease (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref38">38</xref>, <xref ref-type="bibr" rid="ref40">40</xref>, <xref ref-type="bibr" rid="ref43">43</xref>, <xref ref-type="bibr" rid="ref56">56</xref>). Through the lens of hypoketonaemia-ICE, subclinical and clinical hyperinsulinaemia may be detected to within personalised thresholds, where the key determinant is not absolute serum insulin within population intervals, but the functional suppression of ketogenesis (hypoketonaemia).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Conventional versus personalised hyperinsulinaemia threshold.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Feature</th>
<th align="left" valign="top">Conventional hyperinsulinaemia</th>
<th align="left" valign="top">Personalised hyperinsulinaemia threshold (PIT)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Definition</td>
<td align="left" valign="middle">Insulin above population reference interval.</td>
<td align="left" valign="middle">Insulin excess relative to an individual&#x2019;s metabolic baseline, detected through functional markers.</td>
</tr>
<tr>
<td align="left" valign="middle">Primary metric</td>
<td align="left" valign="middle">Absolute serum insulin concentration or HOMA-IR</td>
<td align="left" valign="middle">Chronic evening hypoketonaemia (suppressed beta-hydroxybutyrate) despite adequate 3&#x202F;h post-prandial interval.</td>
</tr>
<tr>
<td align="left" valign="middle">Reference standard</td>
<td align="left" valign="middle">Laboratory-defined population ranges.</td>
<td align="left" valign="middle">Individualised physiological thresholds (insulin&#x2013;ketone relationship).</td>
</tr>
<tr>
<td align="left" valign="middle">Sensitivity</td>
<td align="left" valign="middle">Low, as many patients with early metabolic disease remain within &#x201C;normal&#x201D; insulin ranges.</td>
<td align="left" valign="middle">High, as suppressed ketogenesis reveals insulin toxicity even with apparently normal glucose and insulin values.</td>
</tr>
<tr>
<td align="left" valign="middle">Specificity</td>
<td align="left" valign="middle">Limited, as elevated insulin may reflect transient or context-specific variation.</td>
<td align="left" valign="middle">High, as persistent hypoketonaemia directly reflects sustained basal insulin excess.</td>
</tr>
<tr>
<td align="left" valign="middle">Relation to glucose metrics</td>
<td align="left" valign="middle">Detected only once hyperglycaemia emerges.</td>
<td align="left" valign="middle">Detectable during euglycaemia, exposing hidden hypoketonaemia insulin-compensated euglycaemia (ICE).</td>
</tr>
<tr>
<td align="left" valign="middle">Clinical utility</td>
<td align="left" valign="middle">Identifies late-stage metabolic dysfunction.</td>
<td align="left" valign="middle">Enables early detection and intervention before overt hyperglycaemia or changes in HbA1c.</td>
</tr>
<tr>
<td align="left" valign="middle">Core limitation</td>
<td align="left" valign="middle">Fails to recognise subclinical insulin toxicity in &#x201C;normal&#x201D; ranges.</td>
<td align="left" valign="middle">Requires sequential ketone monitoring to reveal functional suppression.</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Personalised hyperinsulinaemia threshold (PIT) reframes the detection of insulin excess by moving beyond population-based thresholds to individualised metabolic assessment. Through the framework of hypoketonaemia insulin-compensated euglycaemia (ICE), PIT is identified by the persistent suppression of hepatic ketogenesis (hypoketonaemia), most sensitively captured by evening beta-hydroxybutyrate measurement a 3-h post-prandial interval. Unlike conventional definitions of hyperinsulinaemia, which rely on absolute serum insulin concentrations or HOMA-IR, PIT identifies insulin toxicity even within apparently regular laboratory reference intervals, thereby providing greater sensitivity and specificity. This approach enables the recognition of early metabolic disease at a stage where fasting glucose and HbA1c remain deceptively normal, offering a more precise window for timely intervention.</p>
</table-wrap-foot>
</table-wrap>
<p>We have previously published the effects of suppressing ketosis in the Ketosis Suppression and Ageing (KetoSAge) cohort, demonstrating changes in biomarkers associated with ageing and chronic disease, including insulin, HOMA-IR, BHB, leptin, IGF-1, thyroid hormone levels, and GGT (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref39">39</xref>, <xref ref-type="bibr" rid="ref41">41</xref>). Building on this foundation, the present study focuses on female-specific endocrine responses, by examining the impact of long-term sustained nutritional ketosis (NK), euketonaemia (BHB&#x202F;&#x2265;&#x202F;0.5&#x2013;5&#x202F;mmol/L), and the suppression of ketosis (SuK) and hypoketonaemia (BHB&#x202F;&#x003C;&#x202F;0.5&#x202F;mmol/L), on key reproductive and hormonal biomarkers. Specifically, we investigated changes in circulating concentrations of SHBG, oestrogen, progesterone, testosterone, luteinising hormone (LH) and follicle-stimulating hormone (FSH), providing novel insights into the metabolic regulation of female hormonal health. SHBG may further refine the detection of SCHI and aid clinicians in determining risk and prognosis in chronic diseases.</p>
</sec>
<sec sec-type="materials|methods" id="sec2">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec3">
<label>2.1</label>
<title>Participant characteristics and study design</title>
<p>Ten lean (weight, 52.99&#x202F;kg&#x202F;&#x00B1;&#x202F;4.24; height, 160.95&#x202F;cm&#x202F;&#x00B1;&#x202F;7.28; BMI, 20.52&#x202F;kg/m<sup>2</sup>&#x202F;&#x00B1;&#x202F;1.39), healthy pre-menopausal women (age, 32.30&#x202F;&#x00B1;&#x202F;8.97&#x202F;years) who habitually followed a ketogenic diet participated in the KetoSAge study. This was an open-labelled, non-randomised crossover trial comprising three phases: baseline nutritional ketosis (NK; Phase 1, P1), suppression of ketosis (SuK; Phase 2, P2) and return to NK following removal of the intervention (Phase 3, P3) as previously described (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>). Participants self-reported adherence to a lifestyle maintaining NK for &#x2265; 6&#x202F;months (mean 3.9&#x202F;&#x00B1;&#x202F;2.3&#x202F;years), providing sufficient time for metabolic adaptations. Baseline characteristics, adherence measures during experimental period, details on ketosis adaptation, and macronutrient composition with statistical analysis between phases, have been reported previously (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>). At the end of each study phase, participants attended the laboratory at 8:00&#x202F;a.m. after a 12-h overnight fast for anthropometric measurements and blood sampling (<xref ref-type="fig" rid="fig1">Figure 1</xref>).</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>KetoSAge study design. Phases 1 and 3 covered the participants&#x2019; habitual nutritional ketosis lifestyle. Phase 2 was the interventional phase aimed at suppressing ketosis (SuK). Each phase was monitored via finger-prick testing of capillary beta-hydroxybutyrate (BHB) concentration (mmol/L). Testing was conducted four times per day, before to consuming any food, at evenly spaced intervals. At the end of each phase, participants underwent a laboratory testing day for body composition and biochemical tests. Participants were given an oral glucose tolerance test (75&#x202F;g glucose in 250&#x202F;mL water) as described in our earlier publication (<xref ref-type="bibr" rid="ref35">35</xref>). Blood samples were taken at seven time points over 5&#x202F;h. Whole-blood glucose and BHB were measured in real time using the Keto-Mojo&#x2122; meter, and a plasma insulin sensitivity assay was conducted later using an enzyme-linked immunosorbent assay (ELISA). Body mass index (BMI); oral glucose tolerance test (OGGT); and respiratory quotient (RQ). Reproduced from (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>), licensed under <ext-link xlink:href="https://creativecommons.org/licenses/by/4.0/" ext-link-type="uri">CC BY 4.0</ext-link>.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Flowchart depicting a 66-day dietary study with phases for Nutritional Ketosis and Suppressed Ketosis across three 21-day periods. It includes lab visits for anthropometric and metabolic testing. The schedule shows testing windows and capillary BHB measurements. Participants are ten lean, healthy females with a BMI of 20.5 &#x00B1; 1.4 kg/m&#x00B2;. Images depict food diary examples for habitual lifestyles and those following UK standard guidelines.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Anthropometric measurements</title>
<p>Upon arrival at the laboratory, height was measured (to the nearest 0.1&#x202F;cm) using a stadiometer (Marsden HM-250P Leicester Height Measure). Body weight was measured (to the nearest 0.1&#x202F;kg), and waist and hip circumference were obtained using a non-stretch anthropometric measuring tape (Seca&#x00AE; 201) while participants stood upright with feet together. The average of three measurements (cm) was used for analysis. Body mass index (BMI) and fat mass were measured by bioelectrical impedance (BIA) using Seca&#x00AE; (mBCA 514 Medical Body Composition Analyser, Gmbh&#x0026;Co. KG, Hamburg, Germany). All measurements were taken following a 12-h overnight fast, with participants being with an empty bladder and wearing standardised light clothing (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>).</p>
</sec>
<sec id="sec5">
<label>2.3</label>
<title>Blood collection and measurement</title>
<p>As previously described, blood was drawn into ethylenediaminetetraacetic acid (EDTA) tubes (BD, Oxford, UK) before being centrifuged at 3,857&#x202F;g for 10&#x202F;min at 4&#x202F;&#x00B0;C (Hettich Universal 320 R, Germany). Blood was also drawn into serum SST&#x2122; II Advance tubes with thrombin rapid clot activator and separation gel (BD, Oxford, UK) and left for 30&#x202F;min at room temperature. Serum tubes were then centrifuged at 3,857&#x202F;&#x00D7;&#x202F;g for 10&#x202F;min at room temperature. Samples were either sent to SYNLAB Belgium (Alexander Fleming, 3&#x2013;6,220 Heppignies&#x2013;Company No: 0453.111.546) for analysis, or aliquoted into cryovial tubes under sterile conditions and stored at &#x2212;80&#x202F;&#x00B0;C for later analysis by Randox Ireland (55 Diamond Road, Crumlin, Co. Antrim, BT29 4QY, company number, NI015738), Clinilabs (London, UK) or in-house testing (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>).</p>
</sec>
<sec id="sec6">
<label>2.4</label>
<title>Blood marker analysis</title>
<p>Serum fasting insulin was measured using a Simple Plex Assay (Ella&#x2122;, Bio-Techne, Minneapolis, USA). Fasted venous whole blood glucose and BHB concentrations were measured using the Keto-Mojo&#x2122; GKI multi-function meter (Keto-Mojo, Napa, CA, USA) (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref57">57</xref>). The glucose ketone index (GKI) was calculated from whole blood readings obtained using a Keto-Mojo&#x2122; meter as follows: GKI&#x202F;=&#x202F;glucose (mmol/L) &#x00F7; ketones (mmol/L). Leptin (DuoSet, R&#x0026;D Systems, Minneapolis, MN, USA) and glucagon-like peptide (GLP-1; Abcam, Cambridge, UK) were quantified by ELISA from frozen serum samples, according to the manufacturer&#x2019;s instructions. IGF-1, GGT, adiponectin, thyroid-stimulating hormone (TSH), free triiodothyronine (T3), reverse T3, thyroxine (T4) were measured externally by SYNALB, and serum iron was measured externally by Randox and Clinilabs as previously reported (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>). Serum oestrogen, progesterone, testosterone, SHBG, LH, and FSH were measured by Clinilabs.</p>
</sec>
<sec id="sec7">
<label>2.5</label>
<title>Statistical analysis</title>
<p>Data were checked for normality using the Shapiro&#x2013;Wilk test. KetoSAge participants between the study phases (P1, P2, P3) were compared using either repeated measures (RM) one-way ANOVA with Tukey&#x2019;s correction for multiple comparisons, or the Friedman test with Dunn&#x2019;s correction for multiple comparisons, depending on the results of the normality tests. When the sphericity of data was not met, Geisser&#x2013;Greenhouse corrections were also added. Graphed data are presented as mean &#x00B1; SD. Data were analysed and graphed using GraphPad Prism (Version 10.6.1, GraphPad, United States).</p>
<p>Additionally, mixed effects models, sensitivity analyses, and their statistical tests were performed using R version 4.4.3. To account for our study&#x2019;s design, linear mixed-effects models with a random intercept for each participant were used to compare the influence of different physiological variables on hormonal markers across the study phases. Sensitivity analyses were also conducted to account for variations in menstrual cycle stages. All models were implemented using the lmerTest:lmer function in R, with hypothesis testing based on Satterthwaite&#x2019;s method for estimating the degrees of freedom and frequentist hypothesis testing. For the participant with hormone levels below the limit of detection, minimal detectable values for oestrogen, progesterone, and testosterone, were imputed. The results remained unchanged following this adjustment; therefore, statistical analyses on oestrogen, progesterone, and testosterone are presented with data of <italic>n&#x202F;=</italic>&#x202F;9.</p>
</sec>
</sec>
<sec sec-type="results" id="sec8">
<label>3</label>
<title>Results</title>
<sec id="sec9">
<label>3.1</label>
<title>Participant characteristics</title>
<p>Participants had a mean BMI of 20.52 (&#x00B1; 1.39&#x202F;kg/m<sup>2</sup>) and a mean fat mass of 14.21&#x202F;kg (&#x00B1; 2.55) at baseline (P1). Key findings from our analysis in KetoSAge participants are presented, as previously reported in KetoSAge studies (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>). Markers of BMI, fat mass, insulin, glucose, BHB, HOMA-IR, GKI, IGF-1, leptin, GLP-1, GGT, and free T3 were statistically significant from P1 to P2, and this trend reversed following P3. Markers of adiponectin, TSH, reverse T3, T4, and iron were not statistically significant. A summary of all markers investigated in this study across all phases is shown below (<xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>BMI, fat mass, fasted insulin, glucose, BHB, HOMA-IR, GKI, leptin, IGF-1, GLP-1, adiponectin, iron, cortisol, TSH, free T3, reverse T3 and T4 across all phases in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Biomarker</th>
<th align="center" valign="top">P1</th>
<th align="center" valign="top">P2</th>
<th align="center" valign="top">P3</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">P1 vs. P2</th>
<th align="center" valign="top">P2 vs. P3</th>
<th align="center" valign="top">P1 vs. P3</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">BMI<break/>(kg/m2)</td>
<td align="char" valign="middle" char="(">20.52<break/>(&#x00B1; 1.39)</td>
<td align="char" valign="middle" char="(">21.54<break/>(&#x00B1; 1.29)</td>
<td align="char" valign="middle" char="(">20.82<break/>(&#x00B1; 1.46)</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.0734</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Fat Mass<break/>(kg)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">14.21<break/>(&#x00B1; 2.55)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">15.88<break/>(&#x00B1; 2.23)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">14.78<break/>(&#x00B1; 2.20)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0018&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.1102</td>
</tr>
<tr>
<td align="left" valign="middle">Insulin<break/>(&#x03BC;IU/mL)</td>
<td align="char" valign="middle" char="(">4.95<break/>(&#x00B1; 1.24)</td>
<td align="char" valign="middle" char="(">9.06<break/>(&#x00B1; 2.13)</td>
<td align="char" valign="middle" char="(">5.62<break/>(&#x00B1; 1.83)</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.5686</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Glucose<break/>(mmol/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">4.23<break/>(&#x00B1; 0.50)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">5.01<break/>(&#x00B1; 0.70)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">4.24<break/>(&#x00B1; 0.28)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0005&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0014&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0016&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.9980</td>
</tr>
<tr>
<td align="left" valign="middle">BHB<break/>(mmol/L)</td>
<td align="char" valign="middle" char="(">2.43<break/>(&#x00B1; 1.28)</td>
<td align="char" valign="middle" char="(">0.18<break/>(&#x00B1; 0.13)</td>
<td align="char" valign="middle" char="(">2.31<break/>(&#x00B1; 0.71)</td>
<td align="char" valign="middle" char=".">0.0002&#x002A;</td>
<td align="char" valign="middle" char=".">0.0012&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.9638</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">HOMA-IR</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">0.93<break/>(&#x00B1; 0.26)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">2.03<break/>(&#x00B1; 0.65)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">1.07<break/>(&#x00B1; 0.40)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0010&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0024&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.4719</td>
</tr>
<tr>
<td align="left" valign="middle">GKI (Lab Day)</td>
<td align="char" valign="middle" char="(">2.23<break/>(&#x00B1; 1.20)</td>
<td align="char" valign="middle" char="(">49.68<break/>(&#x00B1; 42.62)</td>
<td align="char" valign="middle" char="(">1.99<break/>(&#x00B1; 0.60)</td>
<td align="char" valign="middle" char=".">0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.0024&#x002A;</td>
<td align="char" valign="middle" char=".">0.0024&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">IGF-1<break/>(&#x03BC;g/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">149.30<break/>(&#x00B1; 32.96)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">273.40<break/>(&#x00B1; 85.66)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">136.90<break/>(&#x00B1; 39.60)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0015&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0045&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0055&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.4124</td>
</tr>
<tr>
<td align="left" valign="middle">Leptin<break/>(ng/mL)</td>
<td align="char" valign="middle" char="(">4.50<break/>(&#x00B1; 3.66)</td>
<td align="char" valign="middle" char="(">15.08<break/>(&#x00B1; 8.00)</td>
<td align="char" valign="middle" char="(">4.57<break/>(&#x00B1; 3.48)</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.0010&#x002A;</td>
<td align="char" valign="middle" char=".">0.0052&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">GLP-1<break/>(pg/mL)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">1,383.18<break/>(&#x00B1; 911.36)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">576.72<break/>(&#x00B1; 452.43)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">1,471.85<break/>(&#x00B1; 1,066.75)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0075&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0219&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.0219&#x002A;</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle">GGT<break/>(U/L)</td>
<td align="char" valign="middle" char="(">9.60<break/>(&#x00B1;3.13)</td>
<td align="char" valign="middle" char="(">12.40 (&#x00B1;2.55)</td>
<td align="char" valign="middle" char="(">9.70<break/>(&#x00B1;2.50)</td>
<td align="char" valign="middle" char=".">0.0021&#x002A;</td>
<td align="char" valign="middle" char=".">0.0044&#x002A;</td>
<td align="char" valign="middle" char=".">0.0059&#x002A;</td>
<td align="char" valign="middle" char=".">0.9904</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Adiponectin<break/>(&#x03BC;g/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">9.08<break/>(&#x00B1; 4.18)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">10.75<break/>(&#x00B1; 6.76)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">8.70<break/>(&#x00B1; 3.25)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.4362</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.5391</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">TSH<break/>(mIU/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">1.40<break/>(&#x00B1; 0.74)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">1.56<break/>(&#x00B1; 0.75)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">1.25<break/>(&#x00B1; 0.81)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.3146</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.7135</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.2833</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.7195</td>
</tr>
<tr>
<td align="left" valign="middle">Free T3<break/>(pmol/L)</td>
<td align="char" valign="middle" char="(">3.82<break/>(&#x00B1; 0.28)</td>
<td align="char" valign="middle" char="(">5.50<break/>(&#x00B1; 0.72)</td>
<td align="char" valign="middle" char="(">4.05<break/>(&#x00B1; 0.54)</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">&#x003C;0.0001&#x002A;</td>
<td align="char" valign="middle" char=".">0.0015&#x002A;</td>
<td align="char" valign="middle" char=".">0.2980</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Reverse T3<break/>(nmol/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">0.29<break/>(&#x00B1; 0.09)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">0.26<break/>(&#x00B1; 0.10)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">0.25<break/>(&#x00B1; 0.09)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.5569</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.7181</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.9585</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.5498</td>
</tr>
<tr>
<td align="left" valign="middle">T4<break/>(pmol/L)</td>
<td align="char" valign="middle" char="(">13.52<break/>(&#x00B1; 1.61)</td>
<td align="char" valign="middle" char="(">13.24<break/>(&#x00B1; 1.49)</td>
<td align="char" valign="middle" char="(">12.65<break/>(&#x00B1; 0.66)</td>
<td align="char" valign="middle" char=".">0.2120</td>
<td align="char" valign="middle" char=".">0.8795</td>
<td align="char" valign="middle" char=".">0.3168</td>
<td align="char" valign="middle" char=".">0.2049</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Iron<break/>(&#x03BC;mol/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">16.62<break/>(&#x00B1; 7.27)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">14.40<break/>(&#x00B1; 8.70)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">11.76<break/>(&#x00B1; 11.78)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.1873</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.2209</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.3526</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Beta-hydroxybutyrate (BHB); body mass index (BMI); free triiodothyronine (free T3); gamma-glutamyl transferase (GGT); glucose ketone index (GKI); glucagon-like peptide 1 (GLP-1); homeostasis model assessment for insulin resistance (HOMA-IR); insulin-like growth factor 1 (IGF-1); thyroxine (T4); thyroid stimulating hormone (TSH). Measurements were taken following each of the study phases: baseline nutritional ketosis (NK) P1; intervention to suppress ketosis (SuK) P2; and removal of SuK returning to NK P3. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast. Data was analysed by RM one-way ANOVA or Friedman&#x2019;s test (<italic>n&#x202F;=</italic>&#x202F;10).</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec10">
<label>3.2</label>
<title>Suppression of ketosis is associated with changes in SHBG</title>
<p>Female hormone biomarkers (SHBG, oestrogen, testosterone, progesterone, LH and FSH) across all phases in KetoSAge participants are presented in <xref ref-type="table" rid="tab3">Table 3</xref>. Following P2, SHBG significantly decreased from 107.70&#x202F;nmol/L (&#x00B1; 49.74, P1) to 72.53&#x202F;nmol/L (&#x00B1; 35.43, P2; <italic>p</italic>&#x202F;=&#x202F;0.0060). This trend reversed following P3, where the SHBG significantly returned to participants&#x2019; baseline levels of 111.60&#x202F;nmol/L (&#x00B1; 53.29, P3; <italic>p</italic>&#x202F;=&#x202F;0.0025) compared to P2 (<xref ref-type="table" rid="tab3">Table 3</xref>; <xref ref-type="fig" rid="fig2">Figure 2</xref>). Changes in oestrogen, testosterone, progesterone, LH and FSH were not statistically significant in this study (<xref ref-type="table" rid="tab3">Table 3</xref>; <xref ref-type="fig" rid="fig3">Figures 3</xref>, <xref ref-type="fig" rid="fig4">4</xref>).</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Female hormone biomarker panel in suppression of ketosis.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Biomarker</th>
<th align="center" valign="top">P1</th>
<th align="center" valign="top">P2</th>
<th align="center" valign="top">P3</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
<th align="center" valign="top">P1 vs. P2</th>
<th align="center" valign="top">P2 vs. P3</th>
<th align="center" valign="top">P1 vs. P3</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">SHBG<break/>(nmol/L)</td>
<td align="char" valign="middle" char="(">107.70<break/>(&#x00B1; 49.74)</td>
<td align="char" valign="middle" char="(">72.53<break/>(&#x00B1; 35.43)</td>
<td align="char" valign="middle" char="(">111.60<break/>(&#x00B1; 53.29)</td>
<td align="char" valign="middle" char=".">0.0015&#x002A;</td>
<td align="char" valign="middle" char=".">0.0060&#x002A;</td>
<td align="char" valign="middle" char=".">0.0025&#x002A;</td>
<td align="char" valign="middle" char=".">0.9146</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Oestrogen<break/>(pmol/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">758.30<break/>(&#x00B1; 930.90)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">397.10<break/>(&#x00B1; 242.70)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">545.80<break/>(&#x00B1; 197.30)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.6854</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle">Testosterone<break/>(nmol/L)</td>
<td align="char" valign="middle" char="(">1.16<break/>(&#x00B1; 0.55)</td>
<td align="char" valign="middle" char="(">1.12<break/>(&#x00B1; 0.67)</td>
<td align="char" valign="middle" char="(">1.07<break/>(&#x00B1; 0.51)</td>
<td align="char" valign="middle" char=".">0.8529</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">Progesterone<break/>(nmol/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">4.45<break/>(&#x00B1; 7.54)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">6.77<break/>(&#x00B1; 10.54)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">19.10<break/>(&#x00B1; 22.66)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.9712</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle">LH<break/>(IU/L)</td>
<td align="char" valign="middle" char="(">8.84<break/>(&#x00B1; 12.43)</td>
<td align="char" valign="middle" char="(">13.23<break/>(&#x00B1; 24.39)</td>
<td align="char" valign="middle" char="(">4.57<break/>(&#x00B1; 2.10)</td>
<td align="char" valign="middle" char=".">0.8302</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
<td align="char" valign="middle" char=".">&#x003E;0.9999</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#f3f3f3">FSH<break/>(IU/L)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">5.70<break/>(&#x00B1; 2.85)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">6.92<break/>(&#x00B1; 4.11)</td>
<td align="char" valign="middle" char="(" style="background-color:#f3f3f3">4.66<break/>(&#x00B1; 2.72)</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.3799</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.7252</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.3481</td>
<td align="char" valign="middle" char="." style="background-color:#f3f3f3">0.7908</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Follicle-stimulating hormone (FSH); luteinising hormone (LH); sex hormone binding globulin (SHBG). Measurements were taken following each of the study phases: baseline nutritional ketosis (NK) P1; intervention to suppress ketosis (SuK) P2; and removal of SuK returning to NK P3. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast. Data was analysed by RM one-way ANOVA or Friedman&#x2019;s test (<italic>n&#x202F;=</italic>&#x202F;10; SHBG, LH, FSH, and <italic>n&#x202F;=</italic>&#x202F;9; oestrogen, progesterone, testosterone).</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Levels of sex hormone-binding globulin (SHBG) across all phases in KetoSAge participants. Fasting serum concentrations of SHBG were measured following each of the study phases: baseline nutritional ketosis (NK), P1; intervention to suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. SHBG was measured by Clinilabs, London, UK. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast (<italic>n&#x202F;=</italic>&#x202F;10). Data were analysed by RM one-way ANOVA. &#x002A;&#x002A;<italic>p</italic>&#x202F;&#x003C;&#x202F;0.01.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Line graph showing SHBG levels in nmol/L across three phases: P1, P2, and P3. SHBG levels decrease significantly from P1 to P2, then increase slightly by P3. Asterisks indicate significant differences.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Levels of <bold>(A)</bold> Oestrogen, <bold>(B)</bold> Testosterone and <bold>(C)</bold> Progesterone across all phases in KetoSAge participants. Fasting serum concentrations of oestrogen, testosterone and progesterone were measured following each of the study phases: baseline nutritional ketosis (NK), P1; intervention to suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. Markers were measured by Clinilabs, London, UK. Samples were collected at 8&#x202F;a.m. after a 12&#x202F;h overnight fast (<italic>n&#x202F;=</italic>&#x202F;9), as one participant&#x2019;s hormone levels were below the limit of detection. Data were analysed by Friedman&#x2019;s test.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Graphs display hormone levels across three phases (P1, P2, P3). Chart A shows oestrogen in pmol/L; levels have minimal variation. Chart B displays testosterone in nmol/L; variation is moderate. Chart C shows progesterone in nmol/L, with significant variation in levels.</alt-text>
</graphic>
</fig>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Levels of <bold>(A)</bold> Luteinising Hormone (LH) and <bold>(B)</bold> Follicle-Stimulating Hormone (FSH) across all phases in KetoSAge participants. Fasting serum concentrations of LH and FSH were measured following each of the study phases: baseline nutritional ketosis (NK), P1; intervention to suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. Markers were measured by Clinilabs, London, UK. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast (<italic>n&#x202F;=</italic>&#x202F;10). Data were analysed by Friedman&#x2019;s test or RM one-way ANOVA.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g004.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Two line graphs comparing hormone levels across three phases: P1, P2, and P3. Graph A shows LH levels (IU/L) with wide variation, peaking significantly at P2. Graph B shows FSH levels (IU/L) with scattered variation, also peaking at P2.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec11">
<label>3.3</label>
<title>Relationship of female hormone changes with basal insulin</title>
<p>Data presented in <xref ref-type="table" rid="tab4">Table 4</xref> demonstrate the relationship between female hormonal markers (SHBG, oestrogen, testosterone, progesterone, LH and FSH) and changes in insulin across the three phases and accounting for individual variability among the KetoSAge participants. A significant inverse association was observed between insulin and SHBG [effect estimate (<italic>&#x03B2;</italic>)&#x202F;=&#x202F;&#x2212;7.5030, <italic>p</italic>&#x202F;=&#x202F;0.0010; <xref ref-type="table" rid="tab4">Table 4A</xref>; <xref ref-type="fig" rid="fig5">Figure 5</xref>]. In contrast, the relationships between insulin and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;40.2700, <italic>p</italic>&#x202F;=&#x202F;0.3380), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0183, <italic>p</italic>&#x202F;=&#x202F;0.4800), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1788, <italic>p</italic>&#x202F;=&#x202F;0.8810), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.6588, <italic>p</italic>&#x202F;=&#x202F;0.5800), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.1620, <italic>p</italic>&#x202F;=&#x202F;0.5181) were not statistically significant (<xref ref-type="table" rid="tab4">Table 4A</xref>).</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Change in biomarkers SHBG, oestrogen, testosterone, progesterone, LH and FSH with changes in insulin across all phases in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(A)</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p-</italic>value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ Insulin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;7.5030</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.0010&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ Insulin</td>
<td align="char" valign="bottom" char=".">&#x2212;40.2700</td>
<td align="char" valign="bottom" char=".">0.3380</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ Insulin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.0183</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.4800</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ Insulin</td>
<td align="char" valign="bottom" char=".">&#x2212;0.1788</td>
<td align="char" valign="bottom" char=".">0.8810</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;Insulin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.6588</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.5800</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;Insulin</td>
<td align="char" valign="bottom" char=".">0.1620</td>
<td align="char" valign="bottom" char=".">0.5181</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(B) Log transformed</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p-</italic>value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ Insulin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.6155</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">7.85E-06&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ Insulin</td>
<td align="char" valign="bottom" char=".">&#x2212;0.2505</td>
<td align="center" valign="bottom">0.5290</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ Insulin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.1525</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.3290</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ Insulin</td>
<td align="char" valign="bottom" char=".">&#x2212;0.2915</td>
<td align="center" valign="bottom">0.7330</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;Insulin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.4197</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.3970</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;Insulin</td>
<td align="char" valign="bottom" char=".">0.2793</td>
<td align="center" valign="bottom">0.2848</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Measurements were taken following each of the study phases: baseline nutritional ketosis (NK), P1; intervention suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. Samples were collected at 8&#x202F;a.m. after a 12&#x202F;h overnight fast (<italic>n&#x202F;=</italic>&#x202F;10; SHBG, LH, FSH, and <italic>n&#x202F;=</italic>&#x202F;9; oestrogen, progesterone, testosterone).</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Changes in SHBG with <bold>(A)</bold> insulin, <bold>(B)</bold> HOMA-IR, <bold>(C)</bold> GKI, and <bold>(D)</bold> IGF-1 across all study phases. Analyses were performed and graphs were generated using RStudio with ggplot2.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g005.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Graphs showing changes in SHBG and various metabolic indicators across phases:\n\nA. Insulin levels with SHBG color gradient.\nB. HOMA-IR levels with SHBG color gradient.\nC. GKI values with SHBG color gradient.\nD. IGF-1 levels with SHBG color gradient.\n\nEach graph has a curved line with data points colored from red to blue representing SHBG values from 50 to 150 nmol/L.</alt-text>
</graphic>
</fig>
<p>When data were log-transformed to account for the scale differences across variables, the observed relationships remained consistent. A significant inverse association was observed between insulin and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.6155, <italic>p</italic>&#x202F;=&#x202F;7.85 &#x00D7; 10<sup>&#x2212;6</sup>; <xref ref-type="table" rid="tab4">Table 4B</xref>; <xref ref-type="fig" rid="fig6">Figure 6</xref>). In contrast, the relationships between insulin and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.2505, <italic>p</italic>&#x202F;=&#x202F;0.5290), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1525, <italic>p</italic>&#x202F;=&#x202F;0.3290), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.2915, <italic>p</italic>&#x202F;=&#x202F;0.7330), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.4197, <italic>p</italic>&#x202F;=&#x202F;0.3970), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.2793, <italic>p</italic>&#x202F;=&#x202F;0.2848) were not statistically significant (<xref ref-type="table" rid="tab4">Table 4B</xref>). In both raw data and log-transformed data, SHBG shows a significant inverse association with insulin.</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>Changest in SHBG with <bold>(A)</bold> leptin, <bold>(B)</bold> GLP-1, <bold>(C)</bold> Free T3, and <bold>(D)</bold> GGT) across all study phases. Analysis were performed and graphs were generated using RStudio with ggplot2.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g006.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Graph with four panels illustrating changes in SHBG-related biomarkers across phases. Panel A shows SHBG and Leptin; Panel B depicts SHBG and GLP-1; Panel C presents SHBG and Free T3; Panel D displays SHBG and GGT. Each graph includes lines and colored dots representing different SHBG concentrations, ranging from blue (low) to red (high). The x-axis indicates phases one to three, and the y-axis represents different biomarker levels, varying per graph.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec12">
<label>3.4</label>
<title>Relationship of sex hormone changes with HOMA-IR</title>
<p><xref ref-type="table" rid="tab5">Table 5</xref> shows the relationship between key sex hormone markers (SHBG, oestradiol, testosterone, progesterone, LH, and FSH) and dynamic changes in HOMA-IR across the three intervention phases. These correlations reflect inter-individual variability among KetoSAge participants and capture temporal endocrine adaptations. Similarly, a significant inverse association was observed between HOMA-IR and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;27.4030, <italic>p</italic>&#x202F;=&#x202F;0.0012; <xref ref-type="table" rid="tab5">Table 5A</xref>; <xref ref-type="fig" rid="fig5">Figure 5</xref>). In contrast, the relationships between HOMA-IR and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;169.9000, <italic>p</italic>&#x202F;=&#x202F;0.2790), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0622, <italic>p</italic>&#x202F;=&#x202F;0.5150), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0323, <italic>p</italic>&#x202F;=&#x202F;0.9940), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;2.9110, <italic>p</italic>&#x202F;=&#x202F;0.5140), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.6227, <italic>p</italic>&#x202F;=&#x202F;0.5076) were not statistically significant (<xref ref-type="table" rid="tab5">Table 5A</xref>).</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Change in biomarkers SHBG, oestrogen, testosterone, progesterone, LH and FSH with changes in HOMA-IR across all phases in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(A)</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ HOMA-IR</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;27.4030</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.0012&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ HOMA-IR</td>
<td align="char" valign="bottom" char=".">&#x2212;169.9000</td>
<td align="char" valign="bottom" char=".">0.2790</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ HOMA-IR</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.0622</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.5150</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ HOMA-IR</td>
<td align="char" valign="bottom" char=".">&#x2212;0.0323</td>
<td align="char" valign="bottom" char=".">0.9940</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;HOMA-IR</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">2.9110</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.5140</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;HOMA-IR</td>
<td align="char" valign="bottom" char=".">0.6227</td>
<td align="char" valign="bottom" char=".">0.5076</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(B) Log-transformed</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ HOMA-IR</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.4898</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">7.62E-06&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ HOMA-IR</td>
<td align="char" valign="bottom" char=".">&#x2212;0.3204</td>
<td align="center" valign="bottom">0.3270</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ HOMA-IR</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.1268</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.3070</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ HOMA-IR</td>
<td align="char" valign="bottom" char=".">&#x2212;0.2444</td>
<td align="center" valign="bottom">0.7290</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;HOMA-IR</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.2440</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.5520</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;HOMA-IR</td>
<td align="char" valign="bottom" char=".">0.2372</td>
<td align="center" valign="bottom">0.2720</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>When data were log-transformed to account for the scale differences across variables, the observed relationships remained consistent. A significant inverse association was observed between HOMA-IR and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.4898, <italic>p</italic>&#x202F;=&#x202F;7.62 &#x00D7; 10<sup>&#x2212;6</sup>; <xref ref-type="table" rid="tab5">Table 5B</xref>; <xref ref-type="fig" rid="fig6">Figure 6</xref>). In contrast, the relationships between HOMA-IR and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.3204, <italic>p</italic>&#x202F;=&#x202F;0.3270), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1268, <italic>p</italic>&#x202F;=&#x202F;0.3070), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.2444, <italic>p</italic>&#x202F;=&#x202F;0.7290), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.2440, <italic>p</italic>&#x202F;=&#x202F;0.5520), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.2372, <italic>p</italic>&#x202F;=&#x202F;0.2720) were not statistically significant (<xref ref-type="table" rid="tab5">Table 5B</xref>). In both raw data and log-transformed data, SHBG shows a significant inverse association with HOMA-IR.</p>
</sec>
<sec id="sec13">
<label>3.5</label>
<title>Relationship of sex hormone changes with GKI</title>
<p>Data presented in <xref ref-type="table" rid="tab6">Table 6</xref> show the relationship between female hormone markers (SHBG, oestrogen, testosterone, progesterone, LH and FSH) and changes in GKI across the three phases, accounting for individual variability among the KetoSAge participants. Similarly, a significant inverse association was observed between GKI and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.4102, <italic>p</italic>&#x202F;=&#x202F;0.0183; <xref ref-type="table" rid="tab6">Table 6A</xref>; <xref ref-type="fig" rid="fig5">Figure 5C</xref>). In contrast, the relationships between GKI and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;1.8420, <italic>p</italic>&#x202F;=&#x202F;0.5680), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0009, <italic>p</italic>&#x202F;=&#x202F;0.6520), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0862, <italic>p</italic>&#x202F;=&#x202F;0.3397), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.0103, <italic>p</italic>&#x202F;=&#x202F;0.9099), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.0132, <italic>p</italic>&#x202F;=&#x202F;0.4890) were not statistically significant (<xref ref-type="table" rid="tab6">Table 6A</xref>).</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Change in biomarkers SHBG, oestrogen, testosterone, progesterone, LH and FSH with changes in GKI across all phases in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(A)</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.4102</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.0183&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ GKI</td>
<td align="char" valign="bottom" char=".">&#x2212;1.8420</td>
<td align="char" valign="bottom" char=".">0.5680</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.0009</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.6520</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ GKI</td>
<td align="char" valign="bottom" char=".">&#x2212;0.0862</td>
<td align="char" valign="bottom" char=".">0.3397</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.0103</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.9099</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;GKI</td>
<td align="char" valign="bottom" char=".">0.0132</td>
<td align="char" valign="bottom" char=".">0.4890</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(B) Log-transformed</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.1282</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">1.50E-05&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ GKI</td>
<td align="char" valign="bottom" char=".">&#x2212;0.1336</td>
<td align="center" valign="bottom">0.1790</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.0290</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.3980</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ GKI</td>
<td align="char" valign="bottom" char=".">&#x2212;0.1422</td>
<td align="center" valign="bottom">0.5073</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.1072</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.3900</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;GKI</td>
<td align="char" valign="bottom" char=".">0.0750</td>
<td align="center" valign="bottom">0.2530</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>When data were log-transformed to account for the scale differences across variables, the observed relationships remained consistent. A significant inverse association was observed between GKI and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1282, <italic>p</italic>&#x202F;=&#x202F;1.50 &#x00D7; 10<sup>&#x2212;5</sup>; <xref ref-type="table" rid="tab6">Table 6B</xref>; <xref ref-type="fig" rid="fig7">Figure 7C</xref>). In contrast, the relationships between GKI and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1336, <italic>p</italic>&#x202F;=&#x202F;0.1790), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0290, <italic>p</italic>&#x202F;=&#x202F;0.3980), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1422, <italic>p</italic>&#x202F;=&#x202F;0.5073), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.1072, <italic>p</italic>&#x202F;=&#x202F;0.3900), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.0750, <italic>p</italic>&#x202F;=&#x202F;0.2530) were not statistically significant (<xref ref-type="table" rid="tab6">Table 6B</xref>). In both raw data and log-transformed data, SHBG shows a significant inverse association with GKI.</p>
</sec>
<sec id="sec14">
<label>3.6</label>
<title>Relationship of sex hormone changes with leptin</title>
<p>Data presented in <xref ref-type="table" rid="tab7">Table 7</xref> demonstrate the relationship between female hormone markers (SHBG, oestrogen, testosterone, progesterone, LH and FSH) and changes in leptin across the three phases, accounting for individual variability among the KetoSAge participants. Similarly, a significant inverse association was observed between leptin and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;2.6110, <italic>p</italic>&#x202F;=&#x202F;0.0016; <xref ref-type="table" rid="tab7">Table 7A</xref>; <xref ref-type="fig" rid="fig6">Figure 6A</xref>). In contrast, the relationships between leptin and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;8.6700, <italic>p</italic>&#x202F;=&#x202F;0.5513), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0057, <italic>p</italic>&#x202F;=&#x202F;0.5420), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0586, <italic>p</italic>&#x202F;=&#x202F;0.8863), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.1138, <italic>p</italic>&#x202F;=&#x202F;0.7812), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.0624, <italic>p</italic>&#x202F;=&#x202F;0.4680) were not statistically significant (<xref ref-type="table" rid="tab7">Table 7A</xref>).</p>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Change in biomarkers SHBG, oestrogen, testosterone, progesterone, LH and FSH with changes in leptin across all phases in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(A)</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ Leptin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;2.6110</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.0016&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ Leptin</td>
<td align="char" valign="bottom" char=".">&#x2212;8.6700</td>
<td align="char" valign="bottom" char=".">0.5513</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ Leptin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.0057</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.5420</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ Leptin</td>
<td align="char" valign="bottom" char=".">&#x2212;0.0586</td>
<td align="char" valign="bottom" char=".">0.8863</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;Leptin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.1138</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.7812</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;Leptin</td>
<td align="char" valign="bottom" char=".">0.0624</td>
<td align="char" valign="bottom" char=".">0.4680</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(B) Log-transformed</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ Leptin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.2426</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.0001&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom">Oestrogen ~ Leptin</td>
<td align="char" valign="bottom" char=".">&#x2212;0.1045</td>
<td align="char" valign="bottom" char=".">0.5210</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">Testosterone ~ Leptin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.0973</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.1420</td>
</tr>
<tr>
<td align="left" valign="bottom">Progesterone ~ Leptin</td>
<td align="char" valign="bottom" char=".">0.1841</td>
<td align="char" valign="bottom" char=".">0.5970</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;Leptin</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.1229</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">0.5460</td>
</tr>
<tr>
<td align="left" valign="bottom">FSH&#x202F;~&#x202F;Leptin</td>
<td align="char" valign="bottom" char=".">0.0898</td>
<td align="char" valign="bottom" char=".">0.4030</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Measurements were taken following each of the study phases: baseline nutritional ketosis (NK), P1; intervention suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast (<italic>n&#x202F;=</italic>&#x202F;10; SHBG, LH, FSH, and <italic>n&#x202F;=</italic>&#x202F;9; oestrogen, progesterone, testosterone).</p>
</table-wrap-foot>
</table-wrap>
<fig position="float" id="fig7">
<label>Figure 7</label>
<caption>
<p>Log-transformed changes in SHBG with <bold>(A)</bold> insulin, <bold>(B)</bold> HOMA-IR, <bold>(C)</bold> GKI, and <bold>(D)</bold> IGF-1 across all study phases. Analyses were performed and graphswere generated using RStudio with ggplot2.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g007.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Four line graphs labeled A to D show log-transformed SHBG changes by phase against insulin, HOMA-IR, GKI, and IGF-1. Each graph has three phases on the x-axis and different SHBG levels on the y-axis, with colored markers indicating SHBG concentration. Each graph features gray trend lines with a consistent peak at Phase 2</alt-text>
</graphic>
</fig>
<p>When data were log-transformed to account for the scale differences across variables, the observed relationships remained consistent. A significant inverse association was observed between leptin and SHBG (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.2426, <italic>p</italic>&#x202F;=&#x202F;0.0001; <xref ref-type="table" rid="tab7">Table 7B</xref>; <xref ref-type="fig" rid="fig8">Figure 8A</xref>). In contrast, the relationships between leptin and oestrogen (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1045, <italic>p</italic>&#x202F;=&#x202F;0.5210), testosterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.0973, <italic>p</italic>&#x202F;=&#x202F;0.1420), progesterone (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.1841, <italic>p</italic>&#x202F;=&#x202F;0.5970), LH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.1229, <italic>p</italic>&#x202F;=&#x202F;0.5460), and FSH (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.0898, <italic>p</italic>&#x202F;=&#x202F;0.4030) were not statistically significant (<xref ref-type="table" rid="tab7">Table 7B</xref>). In both raw data and log-transformed data, SHBG shows a significant inverse association with leptin.</p>
<fig position="float" id="fig8">
<label>Figure 8</label>
<caption>
<p>Log-transformed changes in SHBG with <bold>(A)</bold> leptin, <bold>(B)</bold> GLP-1, <bold>(C)</bold> free T3, and <bold>(D)</bold> GGT across all study phases. Analyses were performed and graphs were generated using RStudio with ggplot2.</p>
</caption>
<graphic xlink:href="fnut-12-1731915-g008.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Four line graphs labeled A to D, each depicting log-transformed changes of variables by phase. Graph A shows SHBG and Leptin, B shows SHBG and GLP-1, C shows SHBG and Free T3, and D shows SHBG and GGT. Each graph features colored points on curves representing SHBG levels ranging from blue to red, illustrating fluctuations over three phases.</alt-text>
</graphic>
</fig>
</sec>
<sec id="sec15">
<label>3.7</label>
<title>Relationship of SHBG changes with metabolic parameters</title>
<p><xref ref-type="table" rid="tab8">Table 8</xref> shows the relationship between SHBG and changes in metabolic markers (insulin, HOMA-IR, GKI, leptin, IGF-1, GLP-1, adiponectin, TSH, free T3, reverse T3, and GGT) across the three phases and accounting for individual variability among the KetoSAge participants. As previously reported above, significant inverse associations were observed between SHBG and insulin, HOMA-IR, GKI, and leptin (<xref ref-type="fig" rid="fig5">Figures 5</xref>, <xref ref-type="fig" rid="fig6">6</xref>). Further analyses, including other biomarkers tested, were conducted to assess the relationship between SHBG and the remaining metabolic biomarkers. Statistically significant associations were found between SHBG and IGF-1 (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.1624, <italic>p</italic>&#x202F;=&#x202F;0.0172), GLP-1 (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.0152, <italic>p</italic>&#x202F;=&#x202F;0.0295), free T3 (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;22.4130, p&#x202F;=&#x202F;0.0001), and GGT (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;7.7060, <italic>p</italic>&#x202F;=&#x202F;0.0024; <xref ref-type="table" rid="tab8">Table 8A</xref>; <xref ref-type="fig" rid="fig5">Figures 5</xref>, <xref ref-type="fig" rid="fig6">6</xref>). Adiponectin, TSH, and reverse T3 were not statistically significant (<xref ref-type="table" rid="tab8">Table 8A</xref>).</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Change in SHBG with changes in insulin, HOMA-IR, GKI, leptin, IGF-1, GLP-1, adiponectin, TSH, free T3, reverse T3, and GGT, across all phases in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(A)</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Insulin</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;7.5030</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">0.0010&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ HOMA-IR</td>
<td align="char" valign="middle" char=".">&#x2212;27.4030</td>
<td align="char" valign="middle" char=".">0.0012&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ GKI</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;0.4102</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">0.0183&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ Leptin</td>
<td align="char" valign="middle" char=".">&#x2212;2.6110</td>
<td align="char" valign="middle" char=".">0.0016&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ IGF-1</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;0.1624</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">0.0172&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ GLP-1</td>
<td align="char" valign="middle" char=".">0.0152</td>
<td align="char" valign="middle" char=".">0.0295&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Adiponectin</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;2.0150</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">0.2550</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ TSH</td>
<td align="char" valign="middle" char=".">&#x2212;21.9100</td>
<td align="char" valign="middle" char=".">0.0723</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Free T3</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;22.4130</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">0.0001&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ Reverse T3</td>
<td align="char" valign="middle" char=".">&#x2212;8.1370</td>
<td align="char" valign="middle" char=".">0.9148</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e8e6e6">SHBG ~ GGT</td>
<td align="char" valign="middle" char="." style="background-color:#e8e6e6">&#x2212;7.7060</td>
<td align="char" valign="middle" char="." style="background-color:#e8e6e6">0.0024&#x002A;</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(B) Log transformed</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="center" valign="top">Effect estimate</th>
<th align="center" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Insulin</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;0.6155</td>
<td align="center" valign="middle" style="background-color:#e7e6e6">7.85E-06&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ HOMA-IR</td>
<td align="char" valign="middle" char=".">&#x2212;0.4898</td>
<td align="center" valign="middle">7.62E-06&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ GKI</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.1282</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">1.50E-05&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ Leptin</td>
<td align="char" valign="middle" char=".">&#x2212;0.2426</td>
<td align="center" valign="middle">0.0001&#x002A;</td>
</tr>
<tr>
<td align="left" valign="bottom" style="background-color:#e7e6e6">SHBG ~ IGF-1</td>
<td align="char" valign="bottom" char="." style="background-color:#e7e6e6">&#x2212;0.4163</td>
<td align="center" valign="bottom" style="background-color:#e7e6e6">0.0019&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ GLP-1</td>
<td align="char" valign="middle" char=".">0.2477</td>
<td align="center" valign="middle">0.0015&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Adiponectin</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;0.1651</td>
<td align="center" valign="middle" style="background-color:#e7e6e6">0.4290</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ TSH</td>
<td align="char" valign="middle" char=".">&#x2212;0.1951</td>
<td align="center" valign="middle">0.0899</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Free T3</td>
<td align="char" valign="middle" char="." style="background-color:#e7e6e6">&#x2212;1.1155</td>
<td align="center" valign="middle" style="background-color:#e7e6e6">3.64E-06&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">SHBG ~ Reverse T3</td>
<td align="char" valign="middle" char=".">&#x2212;0.0457</td>
<td align="center" valign="middle">0.8110</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e8e6e6">SHBG ~ GGT</td>
<td align="char" valign="middle" char="." style="background-color:#e8e6e6">&#x2212;0.6835</td>
<td align="center" valign="middle" style="background-color:#e8e6e6">0.0058&#x002A;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Measurements were taken following each of the study phases: baseline nutritional ketosis (NK), P1; intervention suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast; (<italic>n&#x202F;=</italic>&#x202F;10).</p>
</table-wrap-foot>
</table-wrap>
<p>When data were log-transformed to account for the scale differences across variables, the observed relationships remained consistent. As previously reported, significant inverse associations were observed between log-transformed SHBG and insulin, HOMA-IR, GKI, and leptin (<xref ref-type="fig" rid="fig7">Figures 7</xref>, <xref ref-type="fig" rid="fig8">8</xref>). Statistically significant associations were also found between SHBG and IGF-1 (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.4163, <italic>p</italic>&#x202F;=&#x202F;0.0019), GLP-1 (<italic>&#x03B2;</italic>&#x202F;=&#x202F;0.2477, <italic>p</italic>&#x202F;=&#x202F;0.0015), free T3 (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;1.1155, <italic>p</italic>&#x202F;=&#x202F;3.64 &#x00D7; 10<sup>&#x2212;6</sup>), and GGT (<italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;0.6835, <italic>p</italic>&#x202F;=&#x202F;0.0058; <xref ref-type="table" rid="tab8">Table 8B</xref>; <xref ref-type="fig" rid="fig7">Figures 7</xref>, <xref ref-type="fig" rid="fig8">8</xref>). Adiponectin, TSH and reverse T3 were not statistically significant (<xref ref-type="table" rid="tab8">Table 8B</xref>).</p>
</sec>
<sec id="sec16">
<label>3.8</label>
<title>Interaction between sex hormones and the menstrual cycle</title>
<p>To investigate the interaction between female sex hormones and menstrual cycle, an interaction model was constructed including sex hormones, study phase, and menstrual cycle (<xref ref-type="table" rid="tab9">Table 9</xref>). Both raw and log-transformed data for female sex hormones, study phase and stage of the menstrual cycle were analysed. The menstrual cycle stage was not found to be an independent and significantly interacting predictor of female hormone changes in this study.</p>
<table-wrap position="float" id="tab9">
<label>Table 9</label>
<caption>
<p>Changes in SHBG, oestrogen, testosterone, progesterone, LH and FSH accounting for study phase and menstrual cycle in KetoSAge participants.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(A)</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="left" valign="top">Effect estimate</th>
<th align="left" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.4911<break/>Phase: Ovulation: 25.5755</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.9816<break/>Phase: Ovulation: 0.6382</td>
</tr>
<tr>
<td align="left" valign="middle">Oestrogen ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle">Phase: Luteal: &#x2212;117.8300<break/>Phase: Ovulation: 264.9500</td>
<td align="left" valign="middle">Phase: Luteal: 0.6730<break/>Phase: Ovulation: 0.7230</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">Testosterone ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.0219<break/>Phase: Ovulation: &#x2212;0.2524</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.9186<break/>Phase: Ovulation: 0.6412</td>
</tr>
<tr>
<td align="left" valign="middle">Progesterone ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle">Phase: Luteal: 7.7710<break/>Phase: Ovulation: &#x2212;3.5290</td>
<td align="left" valign="middle">Phase: Luteal: 0.1980<break/>Phase: Ovulation: 0.8240</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 2.0620<break/>Phase: Ovulation: &#x2212;34.8570</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.7411<break/>Phase: Ovulation: 0.0504</td>
</tr>
<tr>
<td align="left" valign="middle">FSH&#x202F;~&#x202F;Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle">Phase: Luteal: 0.9143<break/>Phase: Ovulation: &#x2212;8.6900</td>
<td align="left" valign="middle">Phase: Luteal: 0.4250<break/>Phase: Ovulation: 0.0097&#x002A;</td>
</tr>
</tbody>
</table>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" colspan="3">(B) Log-transformed</th>
</tr>
<tr>
<th align="left" valign="top">Model</th>
<th align="left" valign="top">Effect estimate</th>
<th align="left" valign="top"><italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">SHBG ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.0577<break/>Phase: Ovulation: 0.6489</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.7870<break/>Phase: Ovulation: 0.2360</td>
</tr>
<tr>
<td align="left" valign="middle">Oestrogen ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle">Phase: Luteal: &#x2212;0.0080<break/>Phase: Ovulation: 0.4971</td>
<td align="left" valign="middle">Phase: Luteal: 0.9840<break/>Phase: Ovulation: 0.6380</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">Testosterone ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.1181<break/>Phase: Ovulation: 0.0049</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.5250<break/>Phase: Ovulation: 0.9920</td>
</tr>
<tr>
<td align="left" valign="middle">Progesterone ~ Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle">Phase: Luteal: 0.1135<break/>Phase: Ovulation: &#x2212;0.5425</td>
<td align="left" valign="middle">Phase: Luteal: 0.8630<break/>Phase: Ovulation: 0.7590</td>
</tr>
<tr>
<td align="left" valign="middle" style="background-color:#e7e6e6">LH&#x202F;~&#x202F;Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.4450<break/>Phase: Ovulation: &#x2212;1.0549</td>
<td align="left" valign="middle" style="background-color:#e7e6e6">Phase: Luteal: 0.3245<break/>Phase: Ovulation: 0.3939</td>
</tr>
<tr>
<td align="left" valign="middle">FSH&#x202F;~&#x202F;Phase &#x002A; Cycle Stage</td>
<td align="left" valign="middle">Phase: Luteal: 0.1724<break/>Phase: Ovulation: &#x2212;1.0718</td>
<td align="left" valign="middle">Phase: Luteal: 0.3972<break/>Phase: Ovulation: 0.0629</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>Measurements were taken following each of the study phases: baseline nutritional ketosis (NK), P1; intervention suppress ketosis (SuK), P2; and removal of SuK returning to NK, P3. Samples were collected at 8&#x202F;a.m. after a 12-h overnight fast; (<italic>n&#x202F;=</italic>&#x202F;10).</p>
</table-wrap-foot>
</table-wrap>
<p>When analysed relative to the follicular phase, the menstrual cycle stage was a significant predictor of FSH levels (Phase: Luteal, <italic>&#x03B2;</italic>&#x202F;=&#x202F;0.9143 and Phase: Ovulation, <italic>&#x03B2;</italic>&#x202F;=&#x202F;&#x2212;8.6900; Phase: Luteal, <italic>p</italic>&#x202F;=&#x202F;0.4250, and Phase: Ovulation, <italic>p</italic>&#x202F;=&#x202F;0.0097) (<xref ref-type="table" rid="tab9">Table 9A</xref>). However, this significant association became marginal when the data were log-transformed. In the log-transformed model, the menstrual cycle stage was not a significant independent or interacting predictor for any of the female hormone biomarkers assessed (<xref ref-type="table" rid="tab9">Table 9B</xref>).</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec17">
<label>4</label>
<title>Discussion</title>
<p>The female reproductive system is closely regulated by nutritional status and overall energy balance. Metabolic conditions, such as hyperinsulinaemia and insulin resistance can profoundly impact female hormone levels and reproductive function, particularly in conditions like PCOS (<xref ref-type="bibr" rid="ref4">4</xref>, <xref ref-type="bibr" rid="ref59">59</xref>, <xref ref-type="bibr" rid="ref60">60</xref>, <xref ref-type="bibr" rid="ref68">68</xref>). Despite growing interest, the complex pathways by which metabolic health and nutritional signals influence reproductive function remain incompletely understood. Recent studies have investigated the potential role of SHBG, a hepatic glycoprotein, in metabolic dysfunction, due to its inverse relationship with hyperinsulinaemia, insulin resistance and hepatic fat accumulation (<xref ref-type="bibr" rid="ref58 ref59 ref60">58&#x2013;60</xref>). Lower circulating SHBG levels have been correlated with markers of metabolic dysregulation, suggesting its possible utility as a biomarker for hyperinsulinaemia, insulin resistance and metabolic dysfunction-associated steatosis liver disease (MASLD). The present study examined the dynamics of key female hormone biomarkers across three menstrual cycle phases in a cohort of healthy premenopausal women who followed a ketogenic lifestyle with sustained NK (mean duration: 3.9&#x202F;&#x00B1;&#x202F;2.3&#x202F;years). A more precise understanding of how metabolic state influences reproductive hormone regulation is crucial to advancing women&#x2019;s health across the lifespan.</p>
<p>We observed a significant decrease in SHBG levels following suppression of ketosis (P2, hypoketonaemia for 21&#x202F;days), with a subsequent marked return to baseline values upon re-establishment of NK (P3, euketonaemia). Linear mixed-effects modelling further revealed strong associations between SHBG and several metabolic and hormonal markers, including insulin, HOMA-IR, GKI, leptin, IGF-1, GLP-1, free T3 and GGT. These findings are consistent with the role of SHBG as a marker responsive to both insulin signalling and hepatic function (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref23">23</xref>, <xref ref-type="bibr" rid="ref60 ref61 ref62 ref63 ref64 ref65 ref66 ref67">60&#x2013;67</xref>), two systems known to be modulated by long-term ketosis, euketonaemia (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref67">67</xref>, <xref ref-type="bibr" rid="ref68">68</xref>). Observational studies have consistently shown an inverse relationship between SHBG and insulin, with lower SHBG concentrations linked to higher insulin levels and an increased risk of T2DM, independent of circulating sex steroid levels in both men and women (<xref ref-type="bibr" rid="ref66">66</xref>). This inverse relationship is particularly relevant in the context of insulin resistance and hepatic dysfunction, both of which are central features of MetS and are ameliorated through carbohydrate restriction. The return of SHBG to baseline levels upon reintroduction of ketosis further supports the metabolic responsiveness of SHBG and highlights its potential utility as a dynamic biomarker of metabolic-endocrine status in women.</p>
<p>The liver plays a central role in the regulation of systemic insulin sensitivity and sex steroid bioavailability, primarily through its modulation of SHBG synthesis in response to insulin signalling (<xref ref-type="bibr" rid="ref65">65</xref>, <xref ref-type="bibr" rid="ref69">69</xref>). Notably, women with both low SHBG and elevated hepatic fat content have been shown to exhibit the highest insulin concentrations (<xref ref-type="bibr" rid="ref70">70</xref>). This inverse relationship between SHBG and insulin is particularly pronounced among women with greater liver fat accumulation (<xref ref-type="bibr" rid="ref62">62</xref>, <xref ref-type="bibr" rid="ref70">70</xref>), suggesting a compounded metabolic risk in the presence of both hepatic steatosis and decreased SHBG. These findings highlight the liver&#x2019;s central role in integrating metabolic and reproductive functions, a relationship that becomes increasingly relevant in midlife women as oestrogen levels begin to decline. Consistent with this, lower endogenous SHBG levels have been robustly associated with increased risk for cardiometabolic disorders and non-alcoholic fatty liver disease across both sexes and age groups (<xref ref-type="bibr" rid="ref24">24</xref>, <xref ref-type="bibr" rid="ref71">71</xref>, <xref ref-type="bibr" rid="ref72">72</xref>). <italic>In vivo</italic> animal-model studies support a protective role of SHBG in hepatic metabolism. Higher SHBG expression downregulates hepatic ATP production and inhibit lipogenic enzymes, such as acetyl-CoA-carboxylase and fatty acid synthase, thereby reducing hepatic lipid accumulation (<xref ref-type="bibr" rid="ref63">63</xref>, <xref ref-type="bibr" rid="ref73">73</xref>). Further supporting this, SHBG overexpression in transgenic mice protects against high-fat (with carbohydrate) diet (HFD)-induced obesity and insulin resistance, improving glucose tolerance, lowering insulin levels, and attenuating increases in leptin and resistin levels (<xref ref-type="bibr" rid="ref63">63</xref>).</p>
<p>Importantly, the significant differences in SHBG concentrations observed between NK and SuK phases suggest that SHBG may serve as a sensitive marker of metabolic-endocrine interactions within the female hormonal axis. These findings raise important questions about whether fluctuations in SHBG, for instance as those observed from P1 (NK) to P2 (SuK) and subsequently to P3 (return to NK), reflect a compensatory response (homeostatic adaptation) or an active regulatory mechanism in hormone availability in response to altered energy metabolism, particularly under ketogenic conditions or states of metabolic dysfunction. In line with emerging literature, our data support the role of SHBG as both a proxy marker of insulin sensitivity and exposure, hepatic function, as well as a potential diagnostic biomarker and therapeutic target in the management of PCOS, MASLD, and breast cancer (<xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref14">14</xref>). Further research is warranted to elucidate the mechanistic role of SHBG in these contexts and to evaluate its clinical relevance across broader populations.</p>
<p>Dietary carbohydrate restriction, particularly when inducing sustained nutritional ketosis, exerts a profound influence on SHBG concentrations and broader metabolic-endocrine signalling. Across both PCOS and non-PCOS female cohorts, ketogenic interventions that achieve euketonaemia have been consistently associated with elevations in SHBG, accompanied by improvements in glycaemic regulation, insulin sensitivity, and androgen homeostasis (<xref ref-type="bibr" rid="ref18">18</xref>, <xref ref-type="bibr" rid="ref29 ref30 ref31 ref32">29&#x2013;32</xref>, <xref ref-type="bibr" rid="ref68">68</xref>). These outcomes are driven primarily by reductions in basal circulating insulin concentrations. As insulin is the principal suppressor of hepatic ketogenesis, any degree of hyperinsulinaemia, whether overt or subclinical, can suppresses endogenous ketone generation and simultaneously inhibits SHBG synthesis within the liver (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34 ref35 ref36">34&#x2013;36</xref>).</p>
<p>SHBG is a tightly regulated hepatic glycoprotein that binds and transports sex hormones while also serves as a sensitive biomarker of metabolic homeodynamics. Its synthesis is acutely responsive to insulin levels, thyroid hormones, and hepatic fatty acid oxidation status (<xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref66">66</xref>, <xref ref-type="bibr" rid="ref70">70</xref>, <xref ref-type="bibr" rid="ref73">73</xref>). The restoration of higher SHBG levels through nutritional ketosis reflects a shift toward improved metabolic homeodynamics, characterised by sufficiently reduced insulin levels to allow normalisation of hepatic processing and endocrine transport dynamics. Conversely, low SHBG levels are indicative of metabolic dysfunction and actively reflect a state of insulin-excess, often preceding disturbances in reproductive hormones, glucose metabolism, and vascular tone. Thus, SHBG functions as a peripheral surrogate of chronic insulin over-exposure, with reductions in SHBG representing one of the earliest physiological responses to sustained hyperinsulinaemia and hypoketonaemia.</p>
<p>In this context, SHBG offers a unique window into subclinical disease states. The detection of persistently low levels of SHBG in euglycaemic individuals may signal occult hyperinsulinaemia, particularly when ketone levels remain low despite carbohydrate restriction. Such a pattern reveals hepatic insulin resistance before the onset of dysglycaemia, positioning SHBG as a frontline biomarker for the early detection of stage-1 type-2 diabetes and hypoketonaemia-ICE (<xref ref-type="bibr" rid="ref38">38</xref>). This concept aligns with the broader framework in which chronic insulin elevation precedes and drives pathophysiological cascades implicated in cardiovascular disease, oestrogen-dependent cancers, PCOS, and neurodegeneration (<xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref74 ref75 ref76 ref77">74&#x2013;77</xref>).</p>
<p>When oestrogen is coupled with SHBG and both activate their respective receptors on target cells, the resulting intracellular signalling cascade differs from that initiated by oestrogen alone. Many tissues express SHBG membrane receptors, and the binding of SHBG to its receptor induces cyclic adenosine monophosphate (cAMP) activation, functioning as a signal transduction modulator. Furthermore, SHBG molecules can enter cells and exert direct intracellular effects (<xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref14">14</xref>). SHBG downregulates the proto-oncogenes c-Myc, B-cell lymphoma-2 (Bcl-2), and growth factor receptors associated with cancer cell growth, proliferation and survival (<xref ref-type="bibr" rid="ref42">42</xref>, <xref ref-type="bibr" rid="ref78 ref79 ref80">78&#x2013;80</xref>). In contrast, a low SHBG setting results in cancer cells exhibiting increased levels of c-Myc expression, driving transcriptional programmes that promote cellular proliferation, aerobic fermentation, and dedifferentiation. c-Myc upregulates the glucose transporter GLUT1, enabling insulin-independent cellular glucose uptake (<xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref74">74</xref>, <xref ref-type="bibr" rid="ref81 ref82 ref83 ref84">81&#x2013;84</xref>). This enhanced capacity for glucose uptake supplies cancer cell dependence on aerobic fermentation to facilitate their bioenergetic needs via cytosolic substrate-level phosphorylation, independent of insulin mediated glucose uptake signalling (<xref ref-type="bibr" rid="ref42">42</xref>, <xref ref-type="bibr" rid="ref80">80</xref>, <xref ref-type="bibr" rid="ref81">81</xref>, <xref ref-type="bibr" rid="ref85">85</xref>).</p>
<p>Low SHBG levels commonly observed in hyperinsulinaemic and hypoketonaemic states, result in increased c-Myc expression and activity. In cancer cells, higher levels of c-Myc expression drives increased transcription and activity of key glycolytic enzymes, including lactate dehydrogenase (LDH) and hexokinase 2 (HK2) (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref42">42</xref>, <xref ref-type="bibr" rid="ref78">78</xref>, <xref ref-type="bibr" rid="ref80">80</xref>, <xref ref-type="bibr" rid="ref81">81</xref>). LDH plays a central role in sustaining aerobic glycolysis in proliferative tumour phenotypes by catalysing the reduction of pyruvate to lactate (<xref ref-type="bibr" rid="ref80">80</xref>, <xref ref-type="bibr" rid="ref86 ref87 ref88 ref89 ref90">86&#x2013;90</xref>). This reaction regenerates cytosolic nicotinamide adenine dinucleotide (NAD<sup>+</sup>), a redox cofactor essential for the maintenance of glycolytic flux (<xref ref-type="bibr" rid="ref38">38</xref>, <xref ref-type="bibr" rid="ref40">40</xref>, <xref ref-type="bibr" rid="ref43">43</xref>, <xref ref-type="bibr" rid="ref56">56</xref>). The regeneration of NAD<sup>+</sup> permits uninterrupted substrate-level phosphorylation, enabling ATP synthesis independently of mitochondrial oxidative phosphorylation (<xref ref-type="bibr" rid="ref91 ref92 ref93">91&#x2013;93</xref>). This metabolic re-routing underpins the Warburg effect, whereby cancer cells ferment glucose despite sufficient oxygen availability (<xref ref-type="bibr" rid="ref42">42</xref>, <xref ref-type="bibr" rid="ref80">80</xref>). By diverting pyruvate away from mitochondrial entry, LDH functionally suppresses oxidative metabolism, concomitantly enforcing aerobic fermentation of glucose and mitochondrial substrate level phosphorylation of glutamine. This not only sustains ATP production under conditions of mitochondrial dysfunction or high biosynthetic demand but also supports anabolic precursor generation and redox homeostasis necessary for rapid cellular proliferation. The accumulation of extracellular lactate further modifies the tumour microenvironment, promoting immune evasion, angiogenesis, and stromal reprogramming.</p>
<p>HK2 is one of four glucokinase isoforms and catalyses the first committed step of glycolysis by phosphorylating glucose to glucose-6-phosphate, thereby trapping it within the cell. HK2 is predominantly expressed in insulin-responsive tissues, such as skeletal muscle, myocardium, and adipose tissue, where it supports postprandial glucose clearance and energy storage. However, HK2 expression is markedly upregulated in a wide range of malignant tumours, reflecting its dual regulation by insulin signalling and oncogenic drivers (<xref ref-type="bibr" rid="ref94 ref95 ref96">94&#x2013;96</xref>). Activation of the SHBG receptor decreases c-Myc levels and activity, leading to a decrease in HK2 expression. Conversely, low SHBG levels result in increased c-Myc expression, leading to increased HK2 and LDH levels (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref81">81</xref>). HK2 binds directly to the outer mitochondrial membrane, where it utilises ATP generated within the mitochondrial matrix to catalyse the phosphorylation of glucose to glucose-6-phosphate. This spatial proximity, typically mediated via the voltage-dependent anion channel (VDAC), enables HK2 to bypass cytosolic ATP competition and gain privileged access to mitochondrial ATP pools. The mitochondrial anchoring of HK2 permits a high-flux glycolytic state by coupling residual oxidative phosphorylation-derived ATP to the first committed step of glucose metabolism. This arrangement not only accelerates glycolytic throughput but also insulates cancer cells from ATP depletion under fluctuating nutrient conditions. By drawing on mitochondrial energy to support cytosolic glycolysis, HK2 acts as a gatekeeper of bioenergetic partitioning, reinforcing the Warburg phenotype and promoting proliferation under oncogenic and insulin-activated signalling contexts (<xref ref-type="bibr" rid="ref94">94</xref>, <xref ref-type="bibr" rid="ref95">95</xref>, <xref ref-type="bibr" rid="ref97">97</xref>). Hyperinsulinaemia enforces glucose oxidation over beta-oxidation and ketolysis, decreasing the cellular pool of NAD<sup>+</sup>. Concurrently, the mitochondrial derived ATP consumed by HK2 imposes additional mitochondrial demand of NAD<sup>+</sup>, as a result, mitochondrial oxidative phosphorylation is compensatorily reduced to drive up aerobic glycolysis in order to refurnish the cytosol with NAD<sup>+</sup> (<xref ref-type="bibr" rid="ref38">38</xref>, <xref ref-type="bibr" rid="ref43">43</xref>, <xref ref-type="bibr" rid="ref93">93</xref>).</p>
<p>Hyperinsulinaemia decreases SHBG levels, which increases c-Myc expression in cancer cells. Increased c-Myc activity, in turn, upregulates transcription and activity of LDH and HK2 (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref81">81</xref>). Mitochondrial binding of HK2 inhibits Bax-induced cytochrome c release and subsequently inhibits mitochondrial death-receptor-mediated apoptosis (<xref ref-type="bibr" rid="ref98">98</xref>). This strategic positioning enables cancer cells to integrate anabolic demands with survival pathways, reinforcing the glycolytic phenotype characteristic of aggressive, proliferative states (<xref ref-type="bibr" rid="ref96">96</xref>).</p>
<p>Lower levels of SHBG receptor signalling, whilst cells receive oestrogen receptor activation, also increases Bcl-2 levels. Bcl-2 preserves cellular viability by suppressing mitochondrial apoptosis pathways at the level of the outer mitochondrial membrane, preventing the membrane permeabilisation (MOMP) required for cytochrome c release into the cytosol, thereby maintaining the integrity of the mitochondrial intermembrane space and inhibiting caspase activation (<xref ref-type="bibr" rid="ref99">99</xref>). Bcl-2 achieves this through direct interaction with the pro-apoptotic effectors Bax and Bak, sequestering them in inactive conformations and preventing pore formation within the membrane. Through this mechanism, Bcl-2 sustains mitochondrial membrane potential (&#x0394;&#x03C8;m), redox balance, and ATP production, enabling the continued survival of metabolically stressed or oncogenically transformed cells.</p>
<p>Aggressively proliferating cancer cells overexpress Bcl-2, leading to mitochondrial resistance to apoptosis which underpins tumour persistence and therapeutic resistance. Chronic hyperinsulinaemia decreases SHBG, leading to increased Bcl-2 and c-Myc levels. c-Myc expression in cancer cells increases LDH, GLUT1, HK2, and alters transcriptional programming in transformed cells to be more similar to embryonic cells, which is a dedifferentiated, hyperplastic and hyper-prolific state (<xref ref-type="bibr" rid="ref82">82</xref>). This convergence of metabolic and developmental reprogramming reflects mitochondrial pathophysiological transformation and the dominance of insulin&#x2013;glucose&#x2013;growth factor signalling loops.</p>
<p>Given insulin&#x2019;s suppressive control over hepatic SHBG synthesis (<xref ref-type="bibr" rid="ref15">15</xref>, <xref ref-type="bibr" rid="ref100">100</xref>), its measurement offers a mechanistically grounded and clinically accessible metric of metabolic endocrine status. Unlike glucose or HbA1c, which reflect late-stage dysregulation, SHBG, along with euketonaemia monitoring, provides an indirect yet precise index of the insulin axis activity and its broader metabolic-endocrine consequences. This is particularly relevant for women, in whom hypo-SHBG states are predictive of hyperandrogenism, ovulatory dysfunction, and infertility, as well as increased long-term risk for breast and endometrial cancer (<xref ref-type="bibr" rid="ref1">1</xref>, <xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref61">61</xref>). Importantly, SHBG levels increase rapidly in response to dietary carbohydrate restriction, reflecting improved hepatic insulin sensitivity and restoration of fatty acid oxidation (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref83">83</xref>, <xref ref-type="bibr" rid="ref84">84</xref>).</p>
<p>Early restoration of SHBG levels via ketogenic metabolic therapy (KMT) at therapeutic levels, therefore, serves dual functions: as a marker of mechanistic reversal and as a mediator of reduced downstream hormonal and metabolic dysregulation. Its modulation is causally linked to the insulin&#x2013;ketone&#x2013;liver axis and should be integrated into personalised metabolic-endocrine assessments. A shift upwards in SHBG trajectory in the context of euketonaemia and reduced fasting insulin constitutes evidence of improving metabolic-endocrine health, offering an inexpensive, widely accessible surrogate for direct insulin quantification, which remains underutilised and poorly standardised in routine clinical practice (<xref ref-type="bibr" rid="ref16">16</xref>).</p>
<p>SHBG plays a central regulatory role in endocrine&#x2013;oncogenic signalling, with robust mechanistic evidence showing its direct modulation of bioactive sex hormone availability and intracellular receptor activation (<xref ref-type="bibr" rid="ref61">61</xref>). Its suppression by insulin via downregulation of HNF4&#x03B1; links hepatic metabolic status to systemic hormonal control, establishing SHBG as a key mediator at the intersection of metabolic dysfunction, hyperinsulinaemia, and cancer risk (<xref ref-type="bibr" rid="ref61">61</xref>). Reduced SHBG levels increase free testosterone and oestradiol, promoting tumour proliferation in hormone-responsive cancers, while elevated SHBG inhibits growth and metastasis even in receptor-negative phenotypes (<xref ref-type="bibr" rid="ref13">13</xref>, <xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref101">101</xref>). These integrated findings fulfil the criteria for a central regulatory molecule and support the conclusion that SHBG functions as a protective metabolic&#x2013;endocrine marker with system-wide relevance.</p>
<p>Thus, SHBG emerges as a pivotal indicator in the modern metabolic framework. In women, its diagnostic and prognostic relevance spans reproductive, cardiovascular, oncological, and neurological domains (<xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref23">23</xref>, <xref ref-type="bibr" rid="ref59 ref60 ref61">59&#x2013;61</xref>, <xref ref-type="bibr" rid="ref63">63</xref>, <xref ref-type="bibr" rid="ref71">71</xref>). In both metabolic research and clinical intervention alike, its early normalisation through maintaining euketonaemia reflects therapeutic efficacy and the re-establishment of hepatic metabolic control, preceding irreversible disease progression.</p>
<p>A significant elevation in circulating leptin levels was observed following the 21-day suppression of ketosis during Phase 2 (P2), characterised by sustained hypoketonaemia-ICE. Upon re-establishment of nutritional ketosis in Phase 3 (P3), leptin concentrations returned markedly toward baseline in parallel with restored euketonaemia. Linear mixed-effects modelling confirmed a strong inverse association between leptin and SHBG across phases, highlighting the shared sensitivity of both markers to shifts in insulin signalling. These findings are consistent with the role of leptin as a dynamic metabolic-endocrine biomarker of insulin tone and adipose&#x2013;liver axis regulation.</p>
<p>Leptin, the adipocyte-derived cytokine encoded by the Ob gene, functions far beyond its canonical role in energy homeostasis, exerting pro-tumorigenic effects across multiple malignancies through mitogenic, anti-apoptotic, metabolic, and angiogenic mechanisms. Leptin promotes endothelial tube formation and increases vascular permeability by synergising with VEGF and fibroblast growth factor 2 (FGF-2), thereby facilitating tumour vascularisation and the establishment of a permissive vascular&#x2013;stromal interface for cellular invasion (<xref ref-type="bibr" rid="ref102 ref103 ref104">102&#x2013;104</xref>). Within the glioblastoma microenvironment, leptin secreted by tumour cells directly stimulates endothelial growth, mirroring the effects of VEGF and sustaining tumour expansion under hypoxic and nutrient-depleted conditions (<xref ref-type="bibr" rid="ref104">104</xref>, <xref ref-type="bibr" rid="ref105">105</xref>).</p>
<p>Leptin receptor (ObR) signalling also plays a central role in the metabolic reprogramming of cancer cells. In breast, colon, pancreatic, and endometrial cancers, leptin promotes proliferation by activating PI3K&#x2013;Akt, JAK2&#x2013;STAT3, MEK&#x2013;ERK, and JNK signalling cascades (<xref ref-type="bibr" rid="ref106 ref107 ref108 ref109 ref110">106&#x2013;110</xref>). These cascades converge on cell cycle regulators such as cyclin D1 and Mcl-1, inhibit mitochondrial apoptotic checkpoints that are regulated by Bcl-2 and pro-apoptotic effectors (e.g., Bax, Bak), and support the preferential reliance on aerobic glycolysis over oxidative phosphorylation, driven by mitochondrial adaptation, dysfunction, and redox dysregulation. Chronic excess leptin, insulin, and IGF-1 upregulate anti-apoptotic proteins Bcl-2 and Mcl-1, whilst also inhibiting Bax/Bak oligomerisation. This stabilises the &#x0394;&#x03C8;m, preventing the mitochondrial outer MOMP, which inhibits the mitochondrial checkpoint that governs apoptosis initiation (<xref ref-type="bibr" rid="ref109">109</xref>, <xref ref-type="bibr" rid="ref111">111</xref>). In hypothalamic astrocytes, leptin regulates nutrient transporter expression, including GLUT1 and glutamate transporters, providing further evidence of its influence on nutrient uptake and metabolic dysregulation (<xref ref-type="bibr" rid="ref112">112</xref>).</p>
<p>In hormone-sensitive tissues, leptin interacts with insulin and IGF-1 signalling, forming an endocrine-oncogenic triad that supports tumour progression. Hyperglycaemia and insulin excess potentiate leptin-mediated signalling, particularly via IGF1R&#x2013;Akt&#x2013;mTOR activation, further accelerating cell cycle progression and biomass accumulation in breast and mammary epithelial cells (<xref ref-type="bibr" rid="ref106">106</xref>, <xref ref-type="bibr" rid="ref113">113</xref>). In prostate and endometrial cancers, leptin enhances differentiation, proliferation, and invasiveness through transcriptional upregulation of proto-oncogenes and metabolic enzymes (<xref ref-type="bibr" rid="ref111">111</xref>, <xref ref-type="bibr" rid="ref114">114</xref>). Long-term exposure to leptin increases tumour cell viability and shifts the mitochondrial phenotype toward apoptosis evasion and aerobic glycolysis, enabling anabolic growth in nutrient-variable microenvironments (<xref ref-type="bibr" rid="ref115 ref116 ref117 ref118 ref119">115&#x2013;119</xref>).</p>
<p>Leptin&#x2019;s role in metastasis is also increasingly recognised. In pancreatic and ovarian cancers, leptin&#x2013;ObR signalling promotes extracellular matrix remodelling, migration, and invasion through upregulation of matrix metalloproteinase-13, an enzyme that breaks down the extracellular matrix, particularly collagen, and downstream effectors (<xref ref-type="bibr" rid="ref110">110</xref>, <xref ref-type="bibr" rid="ref120">120</xref>). In ovarian cancer cells, leptin maintains stem-like characteristics and drives a more aggressive transcriptional phenotype, explaining the poor prognosis observed in obese patients with leptin resistance or chronic hyperleptinaemia (<xref ref-type="bibr" rid="ref121">121</xref>, <xref ref-type="bibr" rid="ref122">122</xref>). Clinical data further associate high leptin levels with increased prostate cancer risk and unfavourable outcomes in ovarian cancer (<xref ref-type="bibr" rid="ref122 ref123 ref124">122&#x2013;124</xref>).</p>
<p>Leptin and its receptor ObR are overexpressed in malignant brain tumours and display a strong positive correlation with histopathological grade, with the highest levels consistently observed in glioblastoma (GBM), a WHO grade IV astrocytic neoplasm (<xref ref-type="bibr" rid="ref105">105</xref>). This overexpression actively contributes to tumour pathophysiology. Leptin engages proliferative, anti-apoptotic, and migratory signalling via PI3K&#x2013;Akt and JAK2&#x2013;STAT3 cascades, thereby supporting cellular survival within the tumour microenvironment. These signalling axes converge with broader patterns of metabolic reprogramming, redox mal-adaptation, and downregulation of mitochondrial oxidative metabolism, enabling sustained proliferation even under hypoxic or nutrient-depleted conditions (<xref ref-type="bibr" rid="ref104">104</xref>, <xref ref-type="bibr" rid="ref105">105</xref>, <xref ref-type="bibr" rid="ref107">107</xref>, <xref ref-type="bibr" rid="ref111">111</xref>, <xref ref-type="bibr" rid="ref113">113</xref>, <xref ref-type="bibr" rid="ref115">115</xref>, <xref ref-type="bibr" rid="ref116">116</xref>, <xref ref-type="bibr" rid="ref125 ref126 ref127 ref128 ref129">125&#x2013;129</xref>).</p>
<p>Functioning additionally as a pro-angiogenic cytokine, leptin promotes the expression of VEGF and stimulates endothelial tube formation, driving neovascularisation in support of tumour expansion (<xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref103">103</xref>, <xref ref-type="bibr" rid="ref104">104</xref>, <xref ref-type="bibr" rid="ref130">130</xref>). This angiogenic phenotype is especially prominent in GBM, where vascular proliferation ensures perfusion and also establishes a permissive vascular&#x2013;stromal interface for cellular migration and infiltration. Notably, GBM cells possess the capacity for autocrine leptin production, allowing locally secreted leptin to activate ObR on neighbouring or self-same tumour cells. This autocrine loop amplifies survival signalling, suppresses mitochondrial apoptotic checkpoints, and facilitates the autonomous maintenance of ATP production, redox cycling, and anabolic flux, independent of systemic endocrine input (<xref ref-type="bibr" rid="ref104">104</xref>, <xref ref-type="bibr" rid="ref105">105</xref>, <xref ref-type="bibr" rid="ref131">131</xref>).</p>
<p>Within the broader metabolic&#x2013;endocrine context, hyperinsulinaemia and suppression of SHBG frequently co-occur with leptin resistance, wherein elevated leptin reflects compensatory hypersecretion rather than receptor activity (<xref ref-type="bibr" rid="ref132 ref133 ref134 ref135">132&#x2013;135</xref>). In glioblastoma, the leptin&#x2013;ObR axis actively contributes to tumour metabolism, regulating glutamate and glucose transporters, reinforcing reliance on aerobic glycolysis and glutaminolysis over oxidative phosphorylation, driven by mitochondrial adaptation and dysfunction (<xref ref-type="bibr" rid="ref105">105</xref>, <xref ref-type="bibr" rid="ref112">112</xref>, <xref ref-type="bibr" rid="ref131">131</xref>). These endocrine&#x2013;oncogenic dynamics support the therapeutic rationale for KMT for cancer. By raising SHBG, improving leptin sensitivity, and decreasing excess insulin-mediated signalling, nutritional and therapeutic ketosis may attenuate this endocrine, paracrine and autocrine growth axis and re-sensitise tumour cells to apoptotic initiation (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>). Targeting the metabolic dependencies that sustain tumour bioenergetics offers a viable adjunct to existing therapeutic modalities. Based on KMT&#x2019;s multiple mechanisms of action, this provides a rational explanation of how KMT would improve patient responses to standard of care (SOC) oncology treatments, including chemotherapy, immunotherapy, checkpoint inhibitors, receptor inhibitors, immune cell-based vaccines and radiotherapy.</p>
<p>Leptin, thus, acts as a pleiotropic mitokine, coordinating energy-sensing, redox adaptation, apoptosis resistance, and vascular remodelling within the tumour microenvironment. Its downstream effects reinforce the oncogenic landscape established by chronic hyperinsulinaemia, hypoketonaemia and low SHBG. This amplifies proliferative and invasive capacity while inhibiting apoptosis. Therapeutic strategies targeting leptin&#x2013;ObR signalling may restore mitochondrial sensitivity to redox stress and re-establish endocrine regulation, particularly when combined with ketogenic endocrine metabolic oncology (KEMO) therapies that decrease insulin signalling, increase SHBG and euketonaemia (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>, <xref ref-type="bibr" rid="ref42">42</xref>, <xref ref-type="bibr" rid="ref136">136</xref>).</p>
<p>Beyond SHBG and leptin, we also investigated the effects of ketosis suppression on key reproductive hormones, including oestrogen, progesterone, testosterone, LH, and FSH. While no significant changes were detected in circulating levels of oestrogen, progesterone, testosterone, LH, or FSH across the study phases and in linear effects models, FSH concentrations varied significantly by menstrual cycle phase, with elevated levels during ovulation compared to the follicular phase. This aligns with the physiological role of FSH in follicular development and emphasises the importance of accounting for cycle phase in hormonal-endocrine research. These findings are particularly relevant, given the increasing interest in how dietary strategies, such as low-carbohydrate or ketogenic diets, influence female reproductive physiology. While carbohydrate restriction has shown therapeutic benefits in PCOS, characterised by hyperandrogenism and ovulatory dysfunction, its effects in healthy, reproductive-age women remain underexplored. The present data contribute to this emerging field by demonstrating the apparent hormonal resilience of the reproductive axis in response to short-term changes in metabolic state.</p>
<sec id="sec18">
<label>4.1</label>
<title>Strengths and limitations</title>
<p>This study offers a novel investigation into female hormone-related key biomarkers within the context of metabolic health and ketogenic metabolic therapy in a cohort of healthy premenopausal women adapted to long-term nutritional ketosis. A major strength is the within-subject crossover design, which allowed for metabolically distinct phase comparisons and improved internal validity. The cohort&#x2019;s prolonged keto-adaptation offers unique insights into chronic ketosis&#x2019; influence on females&#x2019; endocrine regulation. However, further studies in larger, more diverse populations, including males, older adults, both keto-adapted and non-keto-adapted, and individuals with metabolic disorders or chronic health conditions, such as PCOS, across a wider age range and health spectrum, are warranted to validate and extend our findings.</p>
<p>Although the menstrual cycle stage did not significantly affect most measured biomarkers in this study, a more comprehensive analysis across cycle stages is planned in future publications to clarify cycle-dependent variability, particularly in relation to systemic biomarkers beyond female sex hormones. Such analyses will enhance our understanding of physiological fluctuations and improve biomarker interpretation in premenopausal women. Additional methodological considerations, including power calculations and sample size estimations, have been detailed in our previous work (<xref ref-type="bibr" rid="ref16">16</xref>, <xref ref-type="bibr" rid="ref34">34</xref>, <xref ref-type="bibr" rid="ref35">35</xref>, <xref ref-type="bibr" rid="ref41">41</xref>).</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec19">
<label>5</label>
<title>Conclusion</title>
<p>This study represents the first controlled investigation into the effects of long-term sustained nutritional ketosis, and its deliberate suppression, on SHBG and related metabolic&#x2013;reproductive signalling in healthy premenopausal women. These findings establish SHBG as a dynamic biomarker and regulator within the insulin&#x2013;liver axis, integrating metabolic and reproductive signalling and demonstrating responsiveness to changes in dietary carbohydrate load. Restoration of euketonaemia, achieved through carbohydrate restriction, whilst consuming adequate natural healthy fats, consistently increased SHBG levels, indicating improved hepatic insulin sensitivity and reversal of insulin-driven endocrine suppression. Importantly, this effect occurred without destabilising reproductive hormone profiles, supporting the safety of carbohydrate restriction in premenopausal women. The elevation of SHBG through therapeutic ketosis holds particular relevance for oncology. In multiple tumour contexts, low SHBG states are associated with increased c-Myc and Bcl-2 expression and activity, upregulation of glycolytic effectors GLUT1, LDH and HK2, promoting mitochondrial resistance to apoptosis.</p>
<p>Chronic hyperinsulinaemia suppresses hepatic SHBG synthesis, thereby disinhibiting these oncogenic pathways, driving increased aerobic glycolysis and substrate level phosphorylation dependence, proliferative signalling, and apoptosis resistance. Elevated leptin, under insulin resistance and hyperinsulinaemia, further reinforces this oncogenic landscape by activating mitogenic, anti-apoptotic, and pro-angiogenic pathways (JAK2-STAT3 and PI3K-Akt) and maintaining cancer stem-like phenotypes in many tumour types. Elevation of SHBG through euketonaemia, acts as a counter-regulatory signal that downregulates substrate level phosphorylation dependence and restores apoptotic capacity. These findings support the therapeutic rationale for KMT in cancer, where correction of hyperinsulinaemia and induction of euketonaemia restricts tumour fuel supply, thus reprogramming endocrine-oncogenic signalling through SHBG modulation. Further translational studies are warranted to determine whether SHBG normalisation may serve as both a biomarker and a mediator of therapeutic response in KMT-treated cancer patients. This paper identifies SHBG as a central regulator of endocrine&#x2013;oncogenic signalling, with elevated SHBG functioning as a protective metabolic-endocrine marker. Insulin thus drives the cancer cell phenotype via transcriptional reprogramming and downregulation of mitochondrial apoptosis capability. Leptin, likewise, regulated by insulin and nutritional status, reinforces this oncogenic landscape. Leptin levels rise under insulin resistance and hyperinsulinaemia, amplifying proliferative signalling and further decoupling tumour growth from endocrine restraint.</p>
<p>Finally, this work also advances a refined diagnostic framework for hyperinsulinaemia. We propose that persistent suppression of BHB below 0.5&#x202F;mmol/L before the evening meal, despite normoglycaemia and reference-range insulin, indicates hypoketonaemia-Insulin-Compensated Euglycaemia (ICE), a subclinical state of pathological insulin excess. This functional phenotype, linked to the Personal Hyperinsulinaemia Threshold (PIT), is routinely missed by conventional glucose-centric assessments. By integrating SHBG with fasting insulin, leptin, and ketone-based metabolic phenotyping, both research and clinical frameworks can achieve earlier detection of SCHI. SHBG and leptin, when interpreted in context, provide mechanistically grounded biomarkers that support timely metabolic intervention before the onset of pathology such as cancer progression.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec20">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec sec-type="ethics-statement" id="sec21">
<title>Ethics statement</title>
<p>The studies involving humans were approved by College of Liberal Arts and Sciences Research Ethics Committee, University of Westminster, United Kingdom. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="sec22">
<title>Author contributions</title>
<p>IDC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. LP: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. AS-M: Data curation, Formal analysis, Software, Validation, Visualization, Writing &#x2013; review &#x0026; editing. TD: Writing &#x2013; review &#x0026; editing. TNS: Writing &#x2013; review &#x0026; editing. DL: Writing &#x2013; review &#x0026; editing. NC: Data curation, Software, Visualization, Writing &#x2013; review &#x0026; editing. YK: Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors would like to acknowledge the time and dedication of all participants who participated in this study.</p>
</ack>
<sec sec-type="COI-statement" id="sec23">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec24">
<title>Generative AI statement</title>
<p>The authors declare that no Gen AI was used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec25">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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</ref-list>
<fn-group>
<fn fn-type="custom" custom-type="edited-by" id="fn0002">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/37514/overview">Peter J. Voshol</ext-link>, Independent Researcher, Culemborg, Netherlands</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0003">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1117596/overview">Alex Buga</ext-link>, The Ohio State University, United States</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2879824/overview">Mads Svart</ext-link>, Aarhus University, Denmark</p>
</fn>
</fn-group>
<glossary>
<def-list>
<title>Glossary</title>
<def-item>
<term>Bcl-2</term>
<def>
<p>B-cell lymphoma-2</p>
</def>
</def-item>
<def-item>
<term>BHB</term>
<def>
<p>Beta-hydroxybutyrate</p>
</def>
</def-item>
<def-item>
<term>BIA</term>
<def>
<p>Bioelectrical impedance</p>
</def>
</def-item>
<def-item>
<term>BMI</term>
<def>
<p>Body mass index</p>
</def>
</def-item>
<def-item>
<term>cAMP</term>
<def>
<p>Cyclic adenosine monophosphate</p>
</def>
</def-item>
<def-item>
<term>CoA</term>
<def>
<p>Coenzyme A</p>
</def>
</def-item>
<def-item>
<term>CVD</term>
<def>
<p>Cardiovascular disease</p>
</def>
</def-item>
<def-item>
<term>EDTA</term>
<def>
<p>Ethylenediaminetetraacetic acid</p>
</def>
</def-item>
<def-item>
<term>ELISA</term>
<def>
<p>Enzyme-linked immunosorbent assay</p>
</def>
</def-item>
<def-item>
<term>Free T3</term>
<def>
<p>Free triiodothyronine</p>
</def>
</def-item>
<def-item>
<term>FSH</term>
<def>
<p>Follicle-stimulating hormone</p>
</def>
</def-item>
<def-item>
<term>GBM</term>
<def>
<p>Glioblastoma</p>
</def>
</def-item>
<def-item>
<term>GGT</term>
<def>
<p>Gamma-glutamyl transferase</p>
</def>
</def-item>
<def-item>
<term>GKI</term>
<def>
<p>Glucose ketone index</p>
</def>
</def-item>
<def-item>
<term>GLP-1</term>
<def>
<p>Glucagon-like peptide 1</p>
</def>
</def-item>
<def-item>
<term>HbA1c</term>
<def>
<p>Haemoglobin A1c</p>
</def>
</def-item>
<def-item>
<term>HFD</term>
<def>
<p>High fat diet (with carbohydrate)</p>
</def>
</def-item>
<def-item>
<term>HK2</term>
<def>
<p>Hexokinase 2</p>
</def>
</def-item>
<def-item>
<term>HOMA-IR</term>
<def>
<p>Homeostasis model assessment for insulin resistance</p>
</def>
</def-item>
<def-item>
<term>ICE</term>
<def>
<p>Insulin-Compensated Euglycaemia</p>
</def>
</def-item>
<def-item>
<term>Idh2</term>
<def>
<p>Isocitrate dehydrogenase 2</p>
</def>
</def-item>
<def-item>
<term>IGF-1</term>
<def>
<p>Insulin-like growth factor 1</p>
</def>
</def-item>
<def-item>
<term>IR</term>
<def>
<p>Insulin resistance</p>
</def>
</def-item>
<def-item>
<term>KEMO</term>
<def>
<p>Ketogenic endocrine metabolic oncology</p>
</def>
</def-item>
<def-item>
<term>KMT</term>
<def>
<p>Ketogenic metabolic therapy</p>
</def>
</def-item>
<def-item>
<term>LDH</term>
<def>
<p>Lactate dehydrogenase</p>
</def>
</def-item>
<def-item>
<term>LH</term>
<def>
<p>Luteinising hormone</p>
</def>
</def-item>
<def-item>
<term>MASLD</term>
<def>
<p>Metabolic-dysfunction-associated steatosis liver disease</p>
</def>
</def-item>
<def-item>
<term>MetS</term>
<def>
<p>Metabolic syndrome</p>
</def>
</def-item>
<def-item>
<term>MOMP</term>
<def>
<p>Membrane permeabilisation</p>
</def>
</def-item>
<def-item>
<term>NAD<sup>+</sup></term>
<def>
<p>Nicotinamide adenine dinucleotide</p>
</def>
</def-item>
<def-item>
<term>NK</term>
<def>
<p>Nutritional ketosis</p>
</def>
</def-item>
<def-item>
<term>ObR</term>
<def>
<p>Leptin receptor</p>
</def>
</def-item>
<def-item>
<term>OGTT</term>
<def>
<p>Oral glucose tolerance test</p>
</def>
</def-item>
<def-item>
<term>PCOS</term>
<def>
<p>Polycystic ovarian syndrome</p>
</def>
</def-item>
<def-item>
<term>PIT</term>
<def>
<p>Personalised hyperinsulinaemia threshold</p>
</def>
</def-item>
<def-item>
<term>P1</term>
<def>
<p>Phase 1</p>
</def>
</def-item>
<def-item>
<term>P2</term>
<def>
<p>Phase 2</p>
</def>
</def-item>
<def-item>
<term>P3</term>
<def>
<p>Phase 3</p>
</def>
</def-item>
<def-item>
<term>RM</term>
<def>
<p>Repeated measures</p>
</def>
</def-item>
<def-item>
<term>RQ</term>
<def>
<p>Respiratory quotient</p>
</def>
</def-item>
<def-item>
<term>SCHI</term>
<def>
<p>subclinical hyperinsulinaemia</p>
</def>
</def-item>
<def-item>
<term>SHBG</term>
<def>
<p>Sex hormone-binding globulin</p>
</def>
</def-item>
<def-item>
<term>SOC</term>
<def>
<p>Standard of care</p>
</def>
</def-item>
<def-item>
<term>SuK</term>
<def>
<p>Suppression of ketosis</p>
</def>
</def-item>
<def-item>
<term>T2DM</term>
<def>
<p>Type 2 diabetes mellitus</p>
</def>
</def-item>
<def-item>
<term>T4</term>
<def>
<p>Thyroxine</p>
</def>
</def-item>
<def-item>
<term>TSH</term>
<def>
<p>Thyroid stimulating hormone</p>
</def>
</def-item>
<def-item>
<term>VDAC</term>
<def>
<p>Voltage-dependent anion channel</p>
</def>
</def-item>
<def-item>
<term>VEGF</term>
<def>
<p>Vascular endothelial growth factor</p>
</def>
</def-item>
<def-item>
<term>WHO</term>
<def>
<p>World Health Organisation</p>
</def>
</def-item>
<def-item>
<term>
<italic>&#x03B2;</italic>
</term>
<def>
<p>Effect estimate</p>
</def>
</def-item>
<def-item>
<term>&#x0394;&#x03C8;m</term>
<def>
<p>Mitochondrial membrane potential</p>
</def>
</def-item>
</def-list>
</glossary>
</back>
</article>