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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Cardiovasc. Med.</journal-id>
<journal-title>Frontiers in Cardiovascular Medicine</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Cardiovasc. Med.</abbrev-journal-title>
<issn pub-type="epub">2297-055X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fcvm.2022.863988</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Cardiovascular Medicine</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Declining Levels and Bioavailability of IGF-I in Cardiovascular Aging Associate With QT Prolongation&#x02013;Results From the 1946 British Birth Cohort</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Charalambous</surname> <given-names>Christos</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1605162/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Moon</surname> <given-names>James C.</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Holly</surname> <given-names>Jeff M. P.</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="aff" rid="aff5"><sup>5</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/6931/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Chaturvedi</surname> <given-names>Nishi</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Hughes</surname> <given-names>Alun D.</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/15173/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Captur</surname> <given-names>Gabriella</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<xref ref-type="aff" rid="aff6"><sup>6</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1149336/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>UCL MRC Unit for Lifelong Health and Ageing, University College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff2"><sup>2</sup><institution>UCL Institute of Cardiovascular Science, University College London</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff3"><sup>3</sup><institution>Cardiac MRI Unit, Barts Heart Centre</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff4"><sup>4</sup><institution>National Institute for Health Research (NIHR) Bristol Nutrition Biomedical Research Unit, Level 3, University Hospitals Bristol Education and Research Centre</institution>, <addr-line>Bristol</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff5"><sup>5</sup><institution>Faculty of Health Sciences, School of Translational Health Sciences, Bristol Medical School, Southmead Hospital, University of Bristol</institution>, <addr-line>Bristol</addr-line>, <country>United Kingdom</country></aff>
<aff id="aff6"><sup>6</sup><institution>Cardiology Department, Centre for Inherited Heart Muscle Conditions, The Royal Free Hospital</institution>, <addr-line>London</addr-line>, <country>United Kingdom</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Flavia Prodam, University of Eastern Piedmont, Italy</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Bert Vandenberk, University of Calgary, Canada; Bence Hegyi, University of California, Davis, United States</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Gabriella Captur <email>gabriella.captur&#x00040;ucl.ac.uk</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Cardiac Rhythmology, a section of the journal Frontiers in Cardiovascular Medicine</p></fn></author-notes>
<pub-date pub-type="epub">
<day>22</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>9</volume>
<elocation-id>863988</elocation-id>
<history>
<date date-type="received">
<day>27</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>03</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2022 Charalambous, Moon, Holly, Chaturvedi, Hughes and Captur.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Charalambous, Moon, Holly, Chaturvedi, Hughes and Captur</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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.</p></license> </permissions>
<abstract>
<sec>
<title>Background</title>
<p>As people age, circulating levels of insulin-like growth factors (IGFs) and IGF binding protein 3 (IGFBP-3) decline. In rat cardiomyocytes, IGF-I has been shown to regulate sarcolemmal potassium channel activity and late sodium current thus impacting cardiac repolarization and the heart rate-corrected QT (QTc). However, the relationship between IGFs and IGFBP-3 with the QTc interval in humans, is unknown.</p></sec>
<sec>
<title>Objectives</title>
<p>To examine the association of IGFs and IGFBP-3 with QTc interval in an older age population-based cohort.</p></sec>
<sec>
<title>Methods</title>
<p>Participants were from the 1946 Medical Research Council (MRC) National Survey of Health and Development (NSHD) British birth cohort. Biomarkers from blood samples at age 53 and 60&#x02013;64 years (y, exposures) included IGF-I/II, IGFBP-3, IGF-I/IGFBP-3 ratio and the change (&#x00394;) in marker levels between the 60&#x02013;64 and 53y sampled timepoints. QTc (outcome) was recorded from electrocardiograms at the 60&#x02013;64y timepoint. Generalized linear multivariable models with adjustments for relevant demographic and clinical factors, were used for complete-cases and repeated after multiple imputation.</p></sec>
<sec>
<title>Results</title>
<p>One thousand four hundred forty-eight participants were included (48.3% men; QTc mean 414 ms interquartile range 26 ms). Univariate analysis revealed an association between low IGF-I and IGF-I/IGFBP-3 ratio at 60&#x02013;64y with QTc prolongation [respectively: &#x003B2; &#x02212;0.30 ms/nmol/L, (95% confidence intervals &#x02212;0.44, &#x02212;0.17), <italic>p</italic> &#x0003C; 0.001; &#x003B2;&#x02212;28.9 ms/unit (-41.93, &#x02212;15.50), <italic>p</italic> &#x0003C; 0.001], but not with IGF-II or IGFBP-3. No association with QTc was found for IGF biomarkers sampled at 53y, however both &#x00394;IGF-I and &#x00394;IGF-I/IGFBP-3 ratio were negatively associated with QTc [&#x003B2; &#x02212;0.04 ms/nmol/L (&#x02212;0.08, &#x02212;0.008), <italic>p</italic> = 0.019; &#x003B2; &#x02212;2.44 ms/unit (-4.17, &#x02212;0.67), <italic>p</italic> = 0.007] while &#x00394;IGF-II and &#x00394;IGFBP-3 showed no association. In fully adjusted complete case and imputed models (reporting latter) low IGF-I and IGF-I/IGFBP-3 ratio at 60&#x02013;64y [&#x003B2; &#x02212;0.21 ms/nmol/L (&#x02212;0.39, &#x02212;0.04), <italic>p</italic> = 0.017; &#x003B2; &#x02212;20.14 ms/unit (&#x02212;36.28, &#x02212;3.99), <italic>p</italic> = 0.015], steeper decline in &#x00394;IGF-I [&#x003B2; &#x02212;0.05 ms/nmol/L/10 years (&#x02212;0.10, &#x02212;0.002), <italic>p</italic> = 0.042] and shallower rise in &#x00394;IGF-I/IGFBP-3 ratio over a decade [&#x003B2; &#x02212;2.16 ms/unit/10 years (&#x02212;4.23, &#x02212;0.09), <italic>p</italic> = 0.041], were all independently associated with QTc prolongation. Independent associations with QTc were also confirmed for other previously known covariates: female sex [&#x003B2; 9.65 ms (6.65, 12.65), <italic>p</italic> &#x0003C; 0.001], increased left ventricular mass [&#x003B2; 0.04 ms/g (0.02, 0.06), <italic>p</italic> &#x0003C; 0.001] and blood potassium levels [&#x003B2; &#x02212;5.70 ms/mmol/L (&#x02212;10.23, &#x02212;1.18) <italic>p</italic> = 0.014].</p></sec>
<sec>
<title>Conclusion</title>
<p>Over a decade, in an older age population-based cohort, declining levels and bioavailability of IGF-I associate with prolongation of the QTc interval. As QTc prolongation associates with increased risk for sudden death even in apparently healthy people, further research into the antiarrhythmic effects of IGF-I on cardiomyocytes is warranted.</p></sec></abstract>
<kwd-group>
<kwd>QTc interval prolongation</kwd>
<kwd>cardiac repolarization</kwd>
<kwd>IGF-I (insulin-like growth factor-I)</kwd>
<kwd>IGFBP-3</kwd>
<kwd>IGF-I/IGFBP-3 molar ratio</kwd>
<kwd>IGF-II</kwd>
</kwd-group>
<counts>
<fig-count count="2"/>
<table-count count="4"/>
<equation-count count="2"/>
<ref-count count="88"/>
<page-count count="13"/>
<word-count count="10057"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>The QT interval on a 12-lead echocardiogram (ECG) represents the time taken by ventricular cardiomyocytes to depolarize and repolarize. A prolonged QT interval, and especially the T-wave onset to T-peak (<xref ref-type="bibr" rid="B1">1</xref>), is thought to result from alterations in sympathetic and parasympathetic activity as well as several other risk factors. Since QTc is a measure of ventricular depolarization and repolarization, having a longer than normal QTc interval risks inducing early afterdepolarizations, and possibly also re-entrant excitation and, torsade de pointes (<xref ref-type="bibr" rid="B2">2</xref>&#x02013;<xref ref-type="bibr" rid="B4">4</xref>) ultimately leading to ventricular arrhythmias and ventricular fibrillation (<xref ref-type="bibr" rid="B5">5</xref>). Prolongation of the heart rate corrected QT (QTc) is a well-established risk factor for increased cardiovascular mortality (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>), all-cause mortality and morbidity (<xref ref-type="bibr" rid="B7">7</xref>), even in apparently healthy people (<xref ref-type="bibr" rid="B8">8</xref>). Although many of the factors associated with QTc prolongation have been identified, including female sex (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B10">10</xref>), hypokalemia (<xref ref-type="bibr" rid="B11">11</xref>), left ventricular hypertrophy (<xref ref-type="bibr" rid="B12">12</xref>), hypertension (<xref ref-type="bibr" rid="B13">13</xref>), drug side effects (<xref ref-type="bibr" rid="B2">2</xref>) and genetics (<xref ref-type="bibr" rid="B14">14</xref>, <xref ref-type="bibr" rid="B15">15</xref>), there are still several unknowns. There is now a need to incorporate metabolic biomarkers in our research to understand the pathophysiology of QTc prolongation.</p>
<p>IGF-I regulates somatic growth, reaching its highest levels during teenage years, with levels decreasing with age (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B17">17</xref>) and it is involved in cell proliferation, protein synthesis, nutrient homeostasis and nervous system, liver, kidney and cardiac development (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>). The decline in IGF-I seems to be greater with higher fat mass (<xref ref-type="bibr" rid="B16">16</xref>). IGF-I is also influenced by other hormones, age, sex, diet and nutrition. Previous studies in older age cohorts showed that reduced levels of IGF-I increase the risk of ischemic heart disease and cardiovascular mortality (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B20">20</xref>). Animal work has shown that insulin like growth factor-I (IGF-I) may influence cardiac repolarization, via the phosphatidyl inositol-3 kinase/protein kinase B (PI3-K/Akt) pathway in cardiomyocytes (<xref ref-type="bibr" rid="B21">21</xref>). The PI3-K pathway directly regulates most of the heart&#x00027;s ion channels, including the rapid delayed rectifier potassium channel that specifically influences cardiac repolarization (<xref ref-type="bibr" rid="B22">22</xref>) and in some animal models, consequently the QTc duration (<xref ref-type="bibr" rid="B23">23</xref>). PI3-K was shown to affect many of the channels involved in the action potential duration, late sodium current, calcium current and slow delayed rectifier potassium channels, through its downstream signaling (<xref ref-type="bibr" rid="B22">22</xref>). Yet, little is known about the relationship between IGF-I and QTc duration in humans, particularly in older persons, in whom IGF-I levels are known to decline (<xref ref-type="bibr" rid="B24">24</xref>). The insulin growth factor-family (IGFs), including IGF-I and IGF-II, has a wide range of physiological functions including the regulation of cellular proliferation, apoptosis, protein synthesis and metabolism (<xref ref-type="bibr" rid="B25">25</xref>). IGF-I in the circulation is bound to insulin like growth factor binding protein-3 (IGFBP-3) and therefore the molar ratio between IGF-I and IGFBP-3 indicates IGF-I bioavailability (<xref ref-type="bibr" rid="B26">26</xref>).</p>
<p>We sought to investigate the association between circulating blood levels of IGFs and IGFBP-3 with cardiac repolarization represented by the QTc interval in older age participants of a population-based longitudinal cohort.</p></sec>
<sec sec-type="methods" id="s2">
<title>Methods</title>
<sec>
<title>Study Population</title>
<p>Participants were from the Medical Research Council (MRC) National Survey of Health and Development (NSHD), a birth cohort study comprised of 5,362 individuals born in 1 week in 1946 in Britain. The cohort has been in continuous follow up since birth, with 24 data collection cycles (<xref ref-type="bibr" rid="B27">27</xref>). Briefly, the cohort has been evaluated multi-dimensionally: anthropometrically, socio-economically (manual and non-manual), and in terms of life-style choices (e.g., smoking) and health function (e.g., mental health, cardiovascular and respiratory function) (<xref ref-type="bibr" rid="B27">27</xref>). A consort diagram summarizing the recruitment process for the current study is presented in <xref ref-type="fig" rid="F1">Figure 1</xref>. Previous studies in the NSHD cohort have shown that use of SEP at age 53 as a surrogate for SEP at age 60&#x02013;64 years is both justifiable and sound, first, because the majority of participants were retired by the age of 60 implying no significant SEP shifts between time-points, and second, because it provided a means to backfill the otherwise high SEP missingness in the cohort at age 60&#x02013;64 (<xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>).</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p>Consort diagram of the recruitment process. The Medical Research Council National Survey of Health and Development (NSHD) consists of 5,362 individuals recruited in 1 week in March 1946 in Britain. The exposures of interest here were metabolic markers at 60&#x02013;64 years while the outcome was QTc interval derived from resting 12-lead electrocardiography (ECG) during the same clinic visit. Both pieces of data were available for 1,513 out of the 5,362 participants. The number of participants involved int the study is presented in the figure below. ECG, electrocardiography; IGF-I, insulin-like growth factor 1; IGF-BP3, insulin-like growth factor binding protein 3.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcvm-09-863988-g0001.tif"/>
</fig></sec>
<sec>
<title>Ethical Approval</title>
<p>The 2006&#x02013;2010 NSHD data collection sweep included an in-depth cardiovascular assessment and was granted ethical approval from the Greater Manchester Local Research Ethics Committee and the Scotland Research Ethics Committee (<xref ref-type="bibr" rid="B27">27</xref>) and written informed consent was given by all study participants. All procedures performed were in accordance with the ethical standards of our institutional and/or national research ethics committees and conformed to the 1964 Helsinki declaration and its later amendments or comparable ethical standards.</p></sec>
<sec>
<title>Outcome: QTc Interval on ECG at 60&#x02013;64years</title>
<p>Between 2006 and 2010 when study members were 60&#x02013;64 years (y), British-based NSHD participants who had not been lost to follow-up or withdrawn, were invited to attend a clinic-based assessment that included a 12-lead resting surface ECG for measurement of QT and R-R intervals using validated computerized algorithms (<xref ref-type="bibr" rid="B27">27</xref>). QTc was calculated using Hodges&#x00027; formula (<xref ref-type="bibr" rid="B30">30</xref>):</p>
<disp-formula id="E1"><mml:math id="M1"><mml:mtable columnalign="left"><mml:mtr><mml:mtd><mml:mi>Q</mml:mi><mml:mi>T</mml:mi><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mi>Q</mml:mi><mml:mi>T</mml:mi><mml:mo>&#x0002B;</mml:mo><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>75</mml:mn><mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mrow><mml:mi>H</mml:mi><mml:mi>R</mml:mi><mml:mo>-</mml:mo><mml:mn>60</mml:mn><mml:mtext>&#x000A0;</mml:mtext></mml:mrow><mml:mo stretchy="false">)</mml:mo></mml:mrow><mml:mo>.</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
<p>Those with severe conduction system disease requiring a permanent pacemaker and those with any type of a cardiac implantable electronic device (<italic>n</italic>=8), atrial fibrillation (<italic>n</italic>=54) or taking anti-arrhythmic medications (<italic>n</italic>=14) at this period were excluded.</p></sec>
<sec>
<title>IGF-I, IGF-II and IGFBP-3</title>
<p>Blood samples were collected at age 53y (non-fasting), and age 60&#x02013;64y (fasting), stored at &#x02212;80&#x000B0;C and assayed together. IGF-I and IGF-II and IGFBP-3 concentrations were obtained by radioimmunoassay using standard protocols in the same laboratory, as previously described (<xref ref-type="bibr" rid="B31">31</xref>). The intra- and inter-assay coefficients of variation for IGF-I, IGF-II, and IGFBP-3 have been previously reported (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B32">32</xref>). IGF-I, IGF-II, and IGFBP-3 values were converted from ng/mL to standard SI units (nmol/L) (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>) considering: 1 ng/ml IGF-I= 0 .130 nmol/L IGF-I; 1 ng/ml IGF-II = 0.134 nmol/L IGF-II; and 1 ng/ml IGFBP-3 = 0.036 nmol/L IGFBP-3. IGF-I/IGFBP-3 values were expressed as molar ratios to indicate IGF-I bioavailability. In order to standardize individual biomarkers&#x00027; deviation (Delta, &#x00394;) over the &#x0007E;10-year period, i.e., from their respective initial 53y timepoint concentrations, the serum concentration at the 60&#x02013;64y timepoint was divided by its 53y concentration, as used in previous studies (<xref ref-type="bibr" rid="B35">35</xref>), then multiplied by the year difference between sampled timepoints divided by the maximum possible year difference, and finally multiplied by 100 to obtain a &#x00394; marker score (Equation 1).</p>
<disp-formula id="E2"><label>(1)</label><mml:math id="M2"><mml:mtable columnalign='left'><mml:mtr><mml:mtd><mml:mi>&#x00394;</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>M</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>s</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>M</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mn>60</mml:mn><mml:mo>&#x02212;</mml:mo><mml:mn>64</mml:mn><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>M</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>k</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mn>53</mml:mn><mml:mi>y</mml:mi></mml:mrow></mml:mfrac><mml:mtext>&#x000A0;</mml:mtext><mml:mi>X</mml:mi><mml:mtext>&#x000A0;</mml:mtext></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mtext>&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;&#x000A0;</mml:mtext><mml:mfrac><mml:mrow><mml:mi>Y</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>d</mml:mi><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi><mml:mi>w</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>m</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>Max</mml:mi><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>b</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mi>y</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>s</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mo stretchy='false'>(</mml:mo><mml:mi>i</mml:mi><mml:mo>.</mml:mo><mml:mi>e</mml:mi><mml:mo>.</mml:mo><mml:mtext>&#x000A0;</mml:mtext><mml:mn>11</mml:mn><mml:mo stretchy='false'>)</mml:mo><mml:mtext>&#x000A0;</mml:mtext></mml:mrow></mml:mfrac><mml:mtext>&#x000A0;</mml:mtext><mml:mi>X</mml:mi><mml:mtext>&#x000A0;</mml:mtext><mml:mn>100</mml:mn></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>
</sec>
<sec>
<title>Covariates</title>
<p>Covariates were selected a priori, based on previous studies and added into our models successively, after centering on age, to help with the interpretation of coefficients. Model 1 adjusted for sex; Model 2 included additional adjustments for socioeconomic position (SEP); Model 3 added clinical covariates known to be associated with QTc; and Model 4 added cardiac covariates known to be associated with QTc. The same models were used for all the IGF/IGFBP-3 biomarkers, at both time points, including &#x00394;IGFs.</p>
<p>The sex of participants was recorded as male or female. Participants&#x00027; SEP was evaluated using occupational data from 1989, when they were of active working age, according to the UK Office of Population Censuses and Surveys Registrar General&#x00027;s social class classification and dichotomized into manual or non-manual. Participants&#x00027; weight and height were used to compute body mass index (BMI). Information about medication usage relevant to the QTc, including antibiotics, antihypertensives, antipsychotics and tricyclic antidepressants (<xref ref-type="bibr" rid="B88">88</xref>) was collected through survey instruments and self-reporting along with other relevant clinical information i.e., history of diabetes, heart disease (capturing ischemic heart disease, myocardial infarction, heart failure, heart rhythm abnormality, congenital heart disease, rheumatic heart disease, and other cardiovascular diseases), hypertension, alcohol intake (g per week), physical activity (counting any case of self-reported physical activity carried out over the preceding 4 weeks and 12 months), and smoking into never, ex- or current as previously described (<xref ref-type="bibr" rid="B36">36</xref>&#x02013;<xref ref-type="bibr" rid="B38">38</xref>). Additional blood investigations carried out on samples at 60&#x02013;64y included glucose, glycated hemoglobin A1c, total cholesterol, high- and low-density lipoprotein cholesterol, triglycerides and electrolytes. Assay details have been reported previously (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B40">40</xref>). At the same clinic visit, two-dimensional transthoracic echocardiography was performed to measure left ventricular ejection fraction and mass (<xref ref-type="bibr" rid="B41">41</xref>).</p></sec>
<sec>
<title>Statistical Analysis</title>
<p>Statistical analysis was performed in R Studio version 4.0.2 (RStudio Team 2020). Distribution of data was assessed visually using Q-Q plots, histograms and the Shapiro-Wilk test. Continuous sample variables are expressed as mean &#x000B1; 1 standard deviation (SD) or median (interquartile range) as appropriate; categorical sample variables, as counts and percent. Paired biomarker differences across the decade were investigated by the paired Wilcoxon rank sum test.</p>
<p>As a result of the skewed distribution of continuous QT parameters, generalized linear models (glm) with a gamma distribution and log link were used to investigate the association of IGFs with QTc interval. Model assumptions were verified with regression diagnostics. Multi-collinearity between final model variables was excluded by demonstrating variance inflation factors &#x0003C;3. To determine whether the associations of IGFs and IGFBP-3 with QTc differed by sex or by heart disease, interaction terms for sex and heart disease were tested at the 10% significance level for all exposures and no interactions were found to justify stratification by either sex or heart disease. To account for data missingness, we used multiple imputation to generate missing covariates and re-ran the multivariable models. The predictive mean matching multiple imputation model using chained equations (<xref ref-type="bibr" rid="B42">42</xref>) included all the exposures, covariates and outcomes from the fully adjusted multivariable models, and generated 5 sets of covariates. Regression coefficient estimates, and their associated variance metrics were calculated for each dataset and combined using the Rubin&#x00027;s rule. Strength of evidence for an association was assessed on the basis of the size of the regression coefficients, their confidence interval (CI) and the <italic>p</italic>-value after controlling the false discovery rate at 5%. All tests were 2 sided; <italic>p</italic> &#x0003C; 0.05 was considered statistically significant and no adjustment was made for multiple testing.</p>
<p>We ran sensitivity analyses on the imputed models in which we re-analyzed the association between IGFs and IGFBP-3 with QTc after removing participants with known cardiovascular disease, and in which we additionally adjust for heart rate to avoid any remaining confounding.</p></sec></sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec>
<title>Participant Characteristics</title>
<p>Of the 5,362 originally enrolled into NSHD, 747 were deceased, 570 had emigrated, 853 had withdrawn and 530 were not contactable, leaving 2,662 that were successfully interviewed between 2006 and 2010. Of these 1,513 had contemporaneous ECG for QTc (outcome) and blood tests for the necessary IGFs/IGFBP-3 biomarkers (exposures). A final sample of 1,448 suitable for analysis remained after excluding participants with a device, atrial fibrillation or on anti-arrhythmics. Of these 1,261, 1,261 and 1,257 participants had repeat measures (at 53y and 60&#x02013;64y) available for IGF-I, IGF-II and IGFBP-3, respectively. Clinicodemographic characteristics of study participants are presented in <xref ref-type="table" rid="T1">Table 1</xref>. The population mean QTc was 414 ms (IQR 402&#x02013;428 ms), with 48.3% being male. Sixty-three male participants (9%) had a QTc prolongation, by definition, (&#x0003E;440 ms) compared to 42 female participants (5.6%; QTc &#x0003E;450 ms) (<xref ref-type="bibr" rid="B43">43</xref>), acknowledging the fact that QTc prolongation within the normal range can still be associated with cardiac arrhythmia. Participants with longer QTc intervals had higher BMI, lower resting heart rate, blood potassium, and circulating levels of IGF-I. Missing data for each covariate per exposure-outcome pair are presented in <xref ref-type="supplementary-material" rid="SM1">Supplementary Tables 1, 2</xref>.</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Participant characteristics across the quartiles of QTc.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="left"><bold>All participants <italic>N</italic> &#x0003D; 1,448</bold></th>
<th valign="top" align="center" colspan="4" style="border-bottom: thin solid #000000;"><bold>Quartiles of QTc</bold></th>
</tr>
<tr>
<th/>
<th/>
<th valign="top" align="center"><bold>Quartile 1</bold></th>
<th valign="top" align="center"><bold>Quartile 2</bold></th>
<th valign="top" align="center"><bold>Quartile 3</bold></th>
<th valign="top" align="center"><bold>Quartile 4</bold></th>
</tr>
<tr>
<th/>
<th/>
<th valign="top" align="center"><bold>(345&#x02013;402 ms)</bold><break/><bold><italic>n</italic> &#x0003D; 376</bold></th>
<th valign="top" align="center"><bold>(402&#x02013;414 ms)</bold><break/><bold><italic>n</italic> &#x0003D; 364</bold></th>
<th valign="top" align="center"><bold>(414&#x02013;428 ms)</bold><break/><bold><italic>n</italic> &#x0003D; 355</bold></th>
<th valign="top" align="center"><bold>(428&#x02013;529 ms)</bold><break/><bold><italic>n</italic> &#x0003D; 353</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><bold>QTc (ms)</bold></td>
<td valign="top" align="center">414 (26)</td>
<td valign="top" align="center">394 (11)</td>
<td valign="top" align="center">408 (5)</td>
<td valign="top" align="center">421 (6)</td>
<td valign="top" align="center">439 (14)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Demographics</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Men, <italic>n</italic> (%)</td>
<td valign="top" align="center">700 (48.3)</td>
<td valign="top" align="center">217 (57.7)</td>
<td valign="top" align="center">188 (51.6)</td>
<td valign="top" align="center">162 (45.6)</td>
<td valign="top" align="center">133 (37.7)</td>
</tr>
<tr>
<td valign="top" align="left">SEP 1989<xref ref-type="table-fn" rid="TN1"><sup>a</sup></xref></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x000A0;&#x000A0;&#x000A0;Manual, <italic>n</italic> (%)</td>
<td valign="top" align="center">378 (26.1)</td>
<td valign="top" align="center">90 (18.6)</td>
<td valign="top" align="center">90 (24.7)</td>
<td valign="top" align="center">94 (26.5)</td>
<td valign="top" align="center">104 (29.5)</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x000A0;&#x000A0;&#x000A0;Non-manual, <italic>n</italic> (%)</td>
<td valign="top" align="center">990 (68.4)</td>
<td valign="top" align="center">268 (71.3)</td>
<td valign="top" align="center">255 (70.1)</td>
<td valign="top" align="center">241 (67.9)</td>
<td valign="top" align="center">226 (64.0)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Anthropometric</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">BMI (kg/m<sup>2</sup>)</td>
<td valign="top" align="center">26.9 &#x000B1; 5.8</td>
<td valign="top" align="center">26.4 (5.0)</td>
<td valign="top" align="center">26.8 (6.1)</td>
<td valign="top" align="center">26.8 (5.8)</td>
<td valign="top" align="center">27.5 (6.2)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>ECG</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">SBP (mmHg)</td>
<td valign="top" align="center">136 (22)</td>
<td valign="top" align="center">136 (24)</td>
<td valign="top" align="center">133 (23)</td>
<td valign="top" align="center">133 (22)</td>
<td valign="top" align="center">137 (22)</td>
</tr>
<tr>
<td valign="top" align="left">DBP (mmHg)</td>
<td valign="top" align="center">78 (12)</td>
<td valign="top" align="center">78 (13)</td>
<td valign="top" align="center">78 (12)</td>
<td valign="top" align="center">78 (14)</td>
<td valign="top" align="center">77 (13)</td>
</tr>
<tr>
<td valign="top" align="left">MAP (mmHg)</td>
<td valign="top" align="center">96 (15)</td>
<td valign="top" align="center">97 (15)</td>
<td valign="top" align="center">97 (14)</td>
<td valign="top" align="center">95 (16)</td>
<td valign="top" align="center">96 (15)</td>
</tr>
<tr>
<td valign="top" align="left">Heart rate (ECG)</td>
<td valign="top" align="center">65 (13)</td>
<td valign="top" align="center">66 (12)</td>
<td valign="top" align="center">65 (12)</td>
<td valign="top" align="center">64 (13)</td>
<td valign="top" align="center">63 (16)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Echocardiography</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">LV mass (g)</td>
<td valign="top" align="center">205.6 (94.7)</td>
<td valign="top" align="center">200.5 (89.2)</td>
<td valign="top" align="center">207.0 (81.3)</td>
<td valign="top" align="center">211.3 (107.4)</td>
<td valign="top" align="center">203.9 (97.4)</td>
</tr>
<tr>
<td valign="top" align="left">LV EF (%)</td>
<td valign="top" align="center">65.13 (9.42)</td>
<td valign="top" align="center">65.22 (9.84)</td>
<td valign="top" align="center">64.76 (9.28)</td>
<td valign="top" align="center">65.56 (9.12)</td>
<td valign="top" align="center">64.88 (9.14)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Blood markers</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">IGF-I at 53y (nmol/L)</td>
<td valign="top" align="center">25.2 (10.2)</td>
<td valign="top" align="center">25.2 (9.7)</td>
<td valign="top" align="center">25.2 (9.8)</td>
<td valign="top" align="center">25.2 (10.8)</td>
<td valign="top" align="center">25.0 (10.4)</td>
</tr>
<tr>
<td valign="top" align="left">IGF-II at 53y (nmol/L)</td>
<td valign="top" align="center">99.3 (42.6)</td>
<td valign="top" align="center">99.1 (38.8)</td>
<td valign="top" align="center">97.3 (42.3)</td>
<td valign="top" align="center">97.5 (46.4)</td>
<td valign="top" align="center">102.1 (43.0)</td>
</tr>
<tr>
<td valign="top" align="left">IGFBP-3 at 53y (nmol/L)</td>
<td valign="top" align="center">171.5 (47.8)</td>
<td valign="top" align="center">171.2 (48.8)</td>
<td valign="top" align="center">173.5 (49.0)</td>
<td valign="top" align="center">172.2 (48.2)</td>
<td valign="top" align="center">170.0 (45.2)</td>
</tr>
<tr>
<td valign="top" align="left">IGF-I/IGFBP-3 molar ratio at 53y</td>
<td valign="top" align="center">0.148 (0.065)</td>
<td valign="top" align="center">0.147 (0.068)</td>
<td valign="top" align="center">0.151 (0.061)</td>
<td valign="top" align="center">0.145 (0.064)</td>
<td valign="top" align="center">0.148 (0.065)</td>
</tr>
<tr>
<td valign="top" align="left">IGF-I at 60&#x02013;64y (nmol/L)</td>
<td valign="top" align="center">22.1 (10.0)</td>
<td valign="top" align="center">22.9 (10.1)</td>
<td valign="top" align="center">22.8 (10.8)</td>
<td valign="top" align="center">21.8 (10.6)</td>
<td valign="top" align="center">20.7 (8.9)</td>
</tr>
<tr>
<td valign="top" align="left">IGF-II at 60&#x02013;64y (nmol/L)</td>
<td valign="top" align="center">84.4 (53.3)</td>
<td valign="top" align="center">83.2 (57.9)</td>
<td valign="top" align="center">84.4 (50.3)</td>
<td valign="top" align="center">84.3 (51.7)</td>
<td valign="top" align="center">86.3 (55.3)</td>
</tr>
<tr>
<td valign="top" align="left">IGFBP-3 at 60&#x02013;64y (nmol/L)&#x0002A;</td>
<td valign="top" align="center">120.2 &#x000B1; 30.1</td>
<td valign="top" align="center">120.1 &#x000B1; 30.1</td>
<td valign="top" align="center">121.5 &#x000B1; 30.8</td>
<td valign="top" align="center">117.8 &#x000B1; 28.6</td>
<td valign="top" align="center">121.5 &#x000B1; 30.8</td>
</tr>
<tr>
<td valign="top" align="left">IGF-I/IGFBP-3 molar ratio at 60&#x02013;64y</td>
<td valign="top" align="center">0.188 (0.080)</td>
<td valign="top" align="center">0.195 (0.085)</td>
<td valign="top" align="center">0.190 (0.082)</td>
<td valign="top" align="center">0.189 (0.075)</td>
<td valign="top" align="center">0.172 (0.074)</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGF-I (%)</td>
<td valign="top" align="center">78.7 (37.0)</td>
<td valign="top" align="center">80.4 (36.2)</td>
<td valign="top" align="center">79.0 (37.2)</td>
<td valign="top" align="center">79.4 (43.6)</td>
<td valign="top" align="center">75.3 (36.8)</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGF-II (%)</td>
<td valign="top" align="center">75.6 (51.4)</td>
<td valign="top" align="center">74.9 (54.1)</td>
<td valign="top" align="center">76.0 (46.7)</td>
<td valign="top" align="center">74.8 (54.7)</td>
<td valign="top" align="center">77.3 (49.3)</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGFBP-3 (%)</td>
<td valign="top" align="center">60.4 (22.8)</td>
<td valign="top" align="center">59.3 (24.8)</td>
<td valign="top" align="center">59.9 (22.1)</td>
<td valign="top" align="center">61.4 (23.8)</td>
<td valign="top" align="center">61.3 (21.9)</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGF-I/IGFBP-3 molar ratio</td>
<td valign="top" align="center">1.15 (0.65)</td>
<td valign="top" align="center">1.18 (0.66)</td>
<td valign="top" align="center">1.16 (0.59)</td>
<td valign="top" align="center">1.20 (0.71)</td>
<td valign="top" align="center">1.10 (0.60)</td>
</tr>
<tr>
<td valign="top" align="left">HbA1c (mmol/mol)</td>
<td valign="top" align="center">39 (5)</td>
<td valign="top" align="center">39 (4)</td>
<td valign="top" align="center">39 (5)</td>
<td valign="top" align="center">39 (5)</td>
<td valign="top" align="center">39 (5)</td>
</tr>
<tr>
<td valign="top" align="left">Blood glucose (mg/dl)</td>
<td valign="top" align="center">5.5 (0.9)</td>
<td valign="top" align="center">5.5 (0.75)</td>
<td valign="top" align="center">5.6 (0.9)</td>
<td valign="top" align="center">5.6 (0.9)</td>
<td valign="top" align="center">5.5 (1.0)</td>
</tr>
<tr>
<td valign="top" align="left">HDL (mmol/L)</td>
<td valign="top" align="center">1.54 (0.54)</td>
<td valign="top" align="center">1.52 (0.57)</td>
<td valign="top" align="center">1.55 (0.50)</td>
<td valign="top" align="center">1.55 (0.49)</td>
<td valign="top" align="center">1.54 (0.58)</td>
</tr>
<tr>
<td valign="top" align="left">LDL (mmol/L)</td>
<td valign="top" align="center">3.53 (1.35)</td>
<td valign="top" align="center">3.54 (1.25)</td>
<td valign="top" align="center">3.55 (1.41)</td>
<td valign="top" align="center">3.58 (1.25)</td>
<td valign="top" align="center">3.48 (1.41)</td>
</tr>
<tr>
<td valign="top" align="left">Total cholesterol (mmol/L)</td>
<td valign="top" align="center">5.65 (1.47)</td>
<td valign="top" align="center">5.53 (1.38)</td>
<td valign="top" align="center">5.70 (1.49)</td>
<td valign="top" align="center">5.74 (1.53)</td>
<td valign="top" align="center">5.66 (1.56)</td>
</tr>
<tr>
<td valign="top" align="left">Triglycerides (mmol/L)</td>
<td valign="top" align="center">1.08 (0.77)</td>
<td valign="top" align="center">1.05 (0.73)</td>
<td valign="top" align="center">1.12 (0.81)</td>
<td valign="top" align="center">1.12 (0.69)</td>
<td valign="top" align="center">1.07 (0.75)</td>
</tr>
<tr>
<td valign="top" align="left">Blood potassium (mmol/L)</td>
<td valign="top" align="center">4.23 (0.36)</td>
<td valign="top" align="center">4.26 (0.34)</td>
<td valign="top" align="center">4.24 (0.36)</td>
<td valign="top" align="center">4.22 (0.36)</td>
<td valign="top" align="center">4.20 (0.39)</td>
</tr>
<tr>
<td valign="top" align="left">Blood sodium (mmol/L)</td>
<td valign="top" align="center">140.4 (2.7)</td>
<td valign="top" align="center">140.2 (2.5)</td>
<td valign="top" align="center">140.4 (2.7)</td>
<td valign="top" align="center">140.4 (2.7)</td>
<td valign="top" align="center">140.4 (2.8)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Clinical</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Diabetes, <italic>n</italic> (%)</td>
<td valign="top" align="center">317 (21.9)</td>
<td valign="top" align="center">71 (18.9)</td>
<td valign="top" align="center">83 (22.8)</td>
<td valign="top" align="center">87 (24.5)</td>
<td valign="top" align="center">76 (21.5)</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension, <italic>n</italic> (%)</td>
<td valign="top" align="center">720 (49.7)</td>
<td valign="top" align="center">174 (46.3)</td>
<td valign="top" align="center">183 (50.3)</td>
<td valign="top" align="center">173 (48.7)</td>
<td valign="top" align="center">190 (53.8)</td>
</tr>
<tr>
<td valign="top" align="left">Heart disease, <italic>n</italic> (%)</td>
<td valign="top" align="center">273 (18.9)</td>
<td valign="top" align="center">63 (16.8)</td>
<td valign="top" align="center">60 (16.5)</td>
<td valign="top" align="center">71 (20.0)</td>
<td valign="top" align="center">79 (22.4)</td>
</tr>
<tr>
<td valign="top" align="left">Hyperthyroidism, <italic>n</italic> (%)</td>
<td valign="top" align="center">28 (1.9)</td>
<td valign="top" align="center">10 (2.7)</td>
<td valign="top" align="center">5 (1.4)</td>
<td valign="top" align="center">8 (2.3)</td>
<td valign="top" align="center">5 (1.4)</td>
</tr>
<tr>
<td valign="top" align="left">Hypothyroidism, <italic>n</italic> (%)</td>
<td valign="top" align="center">146 (10.1)</td>
<td valign="top" align="center">37 (9.8)</td>
<td valign="top" align="center">36 (9.9)</td>
<td valign="top" align="center">39 (11.0)</td>
<td valign="top" align="center">34 (9.6)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Smoking</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Ex-smoker, <italic>n</italic> (%)</td>
<td valign="top" align="center">746 (51.5)</td>
<td valign="top" align="center">191 (50.8)</td>
<td valign="top" align="center">201 (55.2)</td>
<td valign="top" align="center">179 (50.4)</td>
<td valign="top" align="center">175 (49.6)</td>
</tr>
<tr>
<td valign="top" align="left">Current smoker, <italic>n</italic> (%)</td>
<td valign="top" align="center">126 (8.7)</td>
<td valign="top" align="center">34 (9.0)</td>
<td valign="top" align="center">24 (6.6)</td>
<td valign="top" align="center">36 (10.1)</td>
<td valign="top" align="center">32 (9.1)</td>
</tr>
<tr>
<td valign="top" align="left">Alcohol intake (g)</td>
<td valign="top" align="center">26.1 (23.7)</td>
<td valign="top" align="center">27.4 (22.6)</td>
<td valign="top" align="center">25.2 (23.7)</td>
<td valign="top" align="center">26.1 (27.9)</td>
<td valign="top" align="center">25.6 (22.5)</td>
</tr>
<tr>
<td valign="top" align="left">Physical activity (4 weeks), <italic>n</italic> (%)<xref ref-type="table-fn" rid="TN2"><sup>b</sup></xref></td>
<td valign="top" align="center">588 (40.6)</td>
<td valign="top" align="center">148 (39.4)</td>
<td valign="top" align="center">139 (38.2)</td>
<td valign="top" align="center">155 (43.7)</td>
<td valign="top" align="center">146 (41.4)</td>
</tr>
<tr>
<td valign="top" align="left">Physical activity (12 months), <italic>n</italic> (%)<xref ref-type="table-fn" rid="TN2"><sup>b</sup></xref></td>
<td valign="top" align="center">1,273 (87.9)</td>
<td valign="top" align="center">337 (89.6)</td>
<td valign="top" align="center">323 (88.7)</td>
<td valign="top" align="center">308 (92.4)</td>
<td valign="top" align="center">305 (86.4)</td>
</tr>
<tr>
<td valign="top" align="left"><bold>Medications</bold></td>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">Antibiotics, <italic>n</italic> (%)</td>
<td valign="top" align="center">31 (2.1)</td>
<td valign="top" align="center">6 (1.6)</td>
<td valign="top" align="center">11 (3.0)</td>
<td valign="top" align="center">8 (2.3)</td>
<td valign="top" align="center">6 (1.7)</td>
</tr>
<tr>
<td valign="top" align="left">Antidepressants, <italic>n</italic> (%)</td>
<td valign="top" align="center">104 (7.2)</td>
<td valign="top" align="center">28 (7.4)</td>
<td valign="top" align="center">24 (6.6)</td>
<td valign="top" align="center">24 (6.8)</td>
<td valign="top" align="center">28 (7.9)</td>
</tr>
<tr>
<td valign="top" align="left">Antihypertensives, <italic>n</italic> (%)</td>
<td valign="top" align="center">354 (24.4)</td>
<td valign="top" align="center">67 (17.8)</td>
<td valign="top" align="center">90 (24.7)</td>
<td valign="top" align="center">87 (24.5)</td>
<td valign="top" align="center">110 (31.2)</td>
</tr>
<tr>
<td valign="top" align="left">Antipsychotics, <italic>n</italic> (%)</td>
<td valign="top" align="center">9 (0.6)</td>
<td valign="top" align="center">1 (0.3)</td>
<td valign="top" align="center">3 (0.8)</td>
<td valign="top" align="center">1 (0.3)</td>
<td valign="top" align="center">4 (1.1)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="TN1">
<label>a</label>
<p><italic> Defined as manual for socioeconomic position (SEP) classes IIIM-V and non-manual for SEP classes I-IIINM</italic>.</p></fn>
<fn id="TN2">
<label>b</label>
<p><italic>Physical activity represents the self-reporting on one form of physical activity undertaken at least once within the previous 4 weeks and 12 months</italic>.</p></fn>
<p><italic>All participants who had both ECG and IGF-I blood tests are presented</italic>.</p>
<p><italic>Results are reported as counts (%) for categorical variables, mean &#x000B1; 1 standard deviation for normally distributed variables (&#x0002A;) or median (interquartile range) for non-normal variables. BMI, body mass index; DBP, diastolic blood pressure; ECG, electrocardiography; EF, ejection fraction; HbA1c, glycated hemoglobin; HDL, high density lipoprotein; IGF-I, insulin-like growth factor-I; IGF-II, insulin-like growth factor-II; IGFBP-3, insulin-like growth factor binding protein 3; LDL, low density lipoprotein; LV, left ventricular; MAP, mean arterial pressure; SBP, systolic blood pressure; SEP, socio-economic position; QTc, corrected QT interval using Hodges&#x00027; formula</italic>.</p>
</table-wrap-foot>
</table-wrap></sec>
<sec>
<title>IGFs and IGFBP-3 at 53y in Relation to QTc Interval at 60&#x02013;64y</title>
<p>On univariate analysis (<xref ref-type="table" rid="T2">Table 2</xref>) none of the IGF variables at 53y was significantly associated with a prolonged QTc interval a decade later (60&#x02013;64y).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Univariate associations between QTc and clinicodemographic data at age 60&#x02013;64.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center" colspan="2" style="border-bottom: thin solid #000000;"><bold>QTc (ms)</bold></th>
</tr>
<tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center"><bold>&#x003B2;-coefficient (95% CI)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">&#x02212;0.14 (&#x02212;1.09, 0.80)</td>
<td valign="top" align="center">0.767</td>
</tr>
<tr>
<td valign="top" align="left">IGF-I at 63y (nmol/L)</td>
<td valign="top" align="center">&#x02212;0.30 (&#x02212;0.44, &#x02212;0.17)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">IGF-II at 63y (nmol/L)</td>
<td valign="top" align="center">&#x02013; 0.007 (&#x02212;0.03, 0.02)</td>
<td valign="top" align="center">0.615</td>
</tr>
<tr>
<td valign="top" align="left">IGFBP-3 at 63y (nmol/L)</td>
<td valign="top" align="center">&#x02013; 0.004 (&#x02212;0.04, 0.03)</td>
<td valign="top" align="center">0.832</td>
</tr>
<tr>
<td valign="top" align="left">IGF-I/IGFBP-3 molar ratio at 60&#x02013;64y</td>
<td valign="top" align="center">&#x02212;28.9 (&#x02212;41.93, &#x02212;15.50)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">IGF-I at 53y (nmol/L)</td>
<td valign="top" align="center">&#x02212;0.05 (&#x02212;0.18, 0.08)</td>
<td valign="top" align="center">0.426</td>
</tr>
<tr>
<td valign="top" align="left">IGF-II at 53y (nmol/L)</td>
<td valign="top" align="center">0.02 (&#x02212;0.02, 0.05)</td>
<td valign="top" align="center">0.349</td>
</tr>
<tr>
<td valign="top" align="left">IGFBP-3 at 53y (nmol/L)</td>
<td valign="top" align="center">&#x02212;0.01 (&#x02212;0.04, 0.02)</td>
<td valign="top" align="center">0.379</td>
</tr>
<tr>
<td valign="top" align="left">IGF-I/IGFBP-3 molar ratio at 53y</td>
<td valign="top" align="center">1.87 (&#x02212;15.17, 19.15)</td>
<td valign="top" align="center">0.832</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGF-I</td>
<td valign="top" align="center">&#x02212;0.04 (&#x02212;0.08, &#x02212;0.008)</td>
<td valign="top" align="center"><bold>0.019</bold></td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGF-II</td>
<td valign="top" align="center">&#x02212;0.01 (&#x02212;0.03, 0.01)</td>
<td valign="top" align="center">0.404</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGFBP-3</td>
<td valign="top" align="center">0.01 (&#x02212;0.04, 0.06)</td>
<td valign="top" align="center">0.734</td>
</tr>
<tr>
<td valign="top" align="left">&#x00394; IGF-I/IGFBP-3 molar ratio</td>
<td valign="top" align="center">&#x02212;2.44 (&#x02212;4.17,&#x02212;0.67)</td>
<td valign="top" align="center"><bold>0.007</bold></td>
</tr>
<tr>
<td valign="top" align="left">HbA1c (mmol/mol)</td>
<td valign="top" align="center">0.05 (&#x02212;0.10, 0.21)</td>
<td valign="top" align="center">0.508</td>
</tr>
<tr>
<td valign="top" align="left">Blood glucose (mg/dl)</td>
<td valign="top" align="center">0.18 (&#x02212;0.74, 1.11)</td>
<td valign="top" align="center">0.708</td>
</tr>
<tr>
<td valign="top" align="left">Sex (male)</td>
<td valign="top" align="center">&#x02212;5.36 (&#x02212;7.48,&#x02212;3.23)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">BMI (kg/m<sup>2</sup>)</td>
<td valign="top" align="center">0.50 (0.26, 0.73)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SEP (non-manual)</td>
<td valign="top" align="center">&#x02212;2.55 (&#x02212;4.86,&#x02212;0.25)</td>
<td valign="top" align="center"><bold>0.030</bold></td>
</tr>
<tr>
<td valign="top" align="left">Smoking:</td>
<td valign="top" align="center">-</td>
<td valign="top" align="center">-</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x000A0;&#x000A0;&#x000A0;Ex-smoker</td>
<td valign="top" align="center">0.15 (&#x02212;2.00, 2.29)</td>
<td valign="top" align="center">0.894</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x000A0;&#x000A0;&#x000A0;Current smoker</td>
<td valign="top" align="center">&#x02212;0.65 (&#x02212;4.42, 3.16)</td>
<td valign="top" align="center">0.737</td>
</tr>
<tr>
<td valign="top" align="left">Heart rate (bpm)</td>
<td valign="top" align="center">&#x02212;0.17 (&#x02212;0.27,&#x02212;0.07)</td>
<td valign="top" align="center"><bold>0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SBP (mmHg)</td>
<td valign="top" align="center">0.03 (&#x02212;0.03, 0.09)</td>
<td valign="top" align="center">0.292</td>
</tr>
<tr>
<td valign="top" align="left">DBP (mmHg)</td>
<td valign="top" align="center">0.03 (&#x02212;0.09, 0.14)</td>
<td valign="top" align="center">0.649</td>
</tr>
<tr>
<td valign="top" align="left">MAP (mmHg)</td>
<td valign="top" align="center">0.03 (&#x02212;0.07, 0.12)</td>
<td valign="top" align="center">0.589</td>
</tr>
<tr>
<td valign="top" align="left">LV mass (g)</td>
<td valign="top" align="center">0.02 (0.006, 0.03)</td>
<td valign="top" align="center"><bold>0.006</bold></td>
</tr>
<tr>
<td valign="top" align="left">LV EF (%)</td>
<td valign="top" align="center">&#x02212;0.06 (&#x02212;0.21, 0.09)</td>
<td valign="top" align="center">0.444</td>
</tr>
<tr>
<td valign="top" align="left">HDL (mmol/L)</td>
<td valign="top" align="center">0.50 (&#x02212;2.15, 3.15)</td>
<td valign="top" align="center">0.715</td>
</tr>
<tr>
<td valign="top" align="left">LDL (mmol/L)</td>
<td valign="top" align="center">0.16 (&#x02212;0.91, 1.22)</td>
<td valign="top" align="center">0.775</td>
</tr>
<tr>
<td valign="top" align="left">Total cholesterol (mmol/L)</td>
<td valign="top" align="center">0.40 (&#x02212;0.50, 1.30)</td>
<td valign="top" align="center">0.384</td>
</tr>
<tr>
<td valign="top" align="left">Triglycerides (mmol/L)</td>
<td valign="top" align="center">0.26 (&#x02212;1.23, 1.76)</td>
<td valign="top" align="center">0.734</td>
</tr>
<tr>
<td valign="top" align="left">Hyperthyroidism</td>
<td valign="top" align="center">&#x02212;2.35 (&#x02212;10.00, 5.48)</td>
<td valign="top" align="center">0.551</td>
</tr>
<tr>
<td valign="top" align="left">Hypothyroidism</td>
<td valign="top" align="center">0.55 (&#x02212;3.01, 4.14)</td>
<td valign="top" align="center">0.762</td>
</tr>
<tr>
<td valign="top" align="left">Blood potassium (mmol/L)</td>
<td valign="top" align="center">&#x02212;8.29 (&#x02212;11.81, &#x02212;4.78)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Blood sodium (mmol/L)</td>
<td valign="top" align="center">0.20 (&#x02212;0.24, 0.65)</td>
<td valign="top" align="center">0.379</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td valign="top" align="center">2.55 (0.42, 4.69)</td>
<td valign="top" align="center"><bold>0.019</bold></td>
</tr>
<tr>
<td valign="top" align="left">Diagnosed Heart disease</td>
<td valign="top" align="center">3.74 (1.00, 6.49)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Alcohol intake (g)</td>
<td valign="top" align="center">&#x02212;0.02 (&#x02212;0.08, 0.04)</td>
<td valign="top" align="center">0.543</td>
</tr>
<tr>
<td valign="top" align="left">Physical activity (4 weeks)</td>
<td valign="top" align="center">&#x02212;0.01 (&#x02212;2.19, 2.17)</td>
<td valign="top" align="center">0.990</td>
</tr>
<tr>
<td valign="top" align="left">Physical activity (12 months)</td>
<td valign="top" align="center">&#x02212;2.21 (&#x02212;5.51, 1.08)</td>
<td valign="top" align="center">0.190</td>
</tr>
<tr>
<td valign="top" align="left">Antibiotics</td>
<td valign="top" align="center">0.30 (&#x02212;1.93, 7.78)</td>
<td valign="top" align="center">0.938</td>
</tr>
<tr>
<td valign="top" align="left">Antidepressants</td>
<td valign="top" align="center">2.25 (&#x02212;1.89, 6.44)</td>
<td valign="top" align="center">0.289</td>
</tr>
<tr>
<td valign="top" align="left">Antihypertensives</td>
<td valign="top" align="center">5.09 (2.60, 7.59)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Antipsychotics</td>
<td valign="top" align="center">10.18 (&#x02212;3.47, 24.42)</td>
<td valign="top" align="center">0.153</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>Analyses are by generalized linear models. Significant p-values are highlighted in bold</italic>.</p>
<p><italic>bpm, beats per minute; CI, confidence interval; &#x003B2;-coefficient, regression coefficient. BMI, body mass index; DBP, diastolic blood pressure; ECG, electrocardiography; EF, ejection fraction; HbA1c, glycated hemoglobin; HDL, high density lipoprotein; IGF-I, insulin-like growth factor-I; IGF-II, insulin-like growth factor-II; IGFBP-3, insulin-like growth factor binding protein 3; LDL, low density lipoprotein; LV, left ventricular; MAP, mean arterial pressure; SBP, systolic blood pressure; SEP, socio-economic position; QTc, corrected QT interval using Hodges&#x00027; formula</italic>.</p>
</table-wrap-foot>
</table-wrap></sec>
<sec>
<title>IGFs and IGFBP-3 at 60&#x02013;64y in Relation to QTc Interval at 60&#x02013;64y</title>
<p>On univariate analysis (<xref ref-type="table" rid="T2">Table 2</xref>) low IGF-I and low IGF-I/IGFBP-3 molar ratio showed an association with QTc prolongation at 60&#x02013;64y (&#x003B2; &#x02212;0.30 ms/nmol/L [&#x02212;0.44, &#x02212;0.17], <italic>p</italic> &#x0003C; 0.001 and &#x003B2; &#x02212;28.9 [&#x02212;41.93, &#x02212;15.50], <italic>p</italic> &#x0003C; 0.001), but IGF-II and IGFBP-3 alone showed no association with QTc. After multivariable adjustment in imputed models, low IGF-I (&#x003B2; &#x02212;0.21 ms/nmol/L [&#x02212;0.39, &#x02212;0.04] <italic>p</italic> = 0.017; representing a multiplicative increase in QTc duration of &#x02248;0.81 ms (exp[-0.21]) per 1 ms/nmol/L decrease in serum IGF-I levels), and low IGF -I/IGFBP-3 molar ratio at 60&#x02013;64y (&#x003B2; &#x02212;20.14 ms/unit [&#x02212;36.28, &#x02212;3.99] <italic>p</italic> = 0.015) were independently associated with QTc (<xref ref-type="table" rid="T3">Table 3</xref>). Complete case analysis support these inferences (<xref ref-type="table" rid="T4">Table 4</xref>).</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Multivariable imputed model exploring the association between IGF-I at 60-64y, IGF-I/IGFBP-3 molar ratio at 60-64y, &#x00394;IGF-I and &#x00394;IGF-I/IGFBP-3 ratio with QTc outcomes (only fully adjusted results for Model 4 are shown here).</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center" colspan="12" style="border-bottom: thin solid #000000;"><bold>QTc (ms) at 60-64y</bold></th>
</tr>
<tr>
<th valign="top" align="left"><bold>Variable</bold></th>
<th/>
<th valign="top" align="center"><bold>&#x003B2;-coefficient (95% CI)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-Value</bold></th>
<th/>
<th valign="top" align="center"><bold>&#x003B2;-coefficient (95% CI)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-Value</bold></th>
<th/>
<th valign="top" align="center"><bold>&#x003B2;-coefficient (95% CI)</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-value</bold></th>
<th/>
<th valign="top" align="center"><bold>&#x003B2;-coefficient (95% CI)</bold></th>
<th valign="top" align="center"><bold>p-value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Biomarker</td>
<td valign="top" align="center"><italic><bold>IGF-I 60&#x02013;64y</bold></italic></td>
<td valign="top" align="center">&#x02212;0.21 (&#x02212;0.39, &#x02212;0.04)</td>
<td valign="top" align="center"><bold>0.017</bold></td>
<td valign="top" align="center"><bold><italic>IGF-I/IGFBP-3</italic></bold><break/><bold><italic>ratio at 60-64y</italic></bold></td>
<td valign="top" align="center">&#x02212;20.14 (&#x02212;36.28, &#x02212;3.99)</td>
<td valign="top" align="center"><bold>0.015</bold></td>
<td valign="top" align="center"><italic><bold>&#x00394;IGF-I</bold></italic></td>
<td valign="top" align="center">&#x02212;0.05 (&#x02212;0.10, &#x02212;0.002)</td>
<td valign="top" align="center"><bold>0.042</bold></td>
<td valign="top" align="center"><bold><italic>&#x00394;IGF-I/</italic></bold><break/><bold><italic>IGFBP-3</italic></bold></td>
<td valign="top" align="center">&#x02212;2.15 (&#x02212;4.23, &#x02212;0.09)</td>
<td valign="top" align="center"><bold>0.041</bold></td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td/>
<td valign="top" align="center">&#x02212;0.31 (&#x02212;1.53, 0.91)</td>
<td valign="top" align="center">0.620</td>
<td/>
<td valign="top" align="center">&#x02212;0.241 (&#x02212;1.47, 0.99)</td>
<td valign="top" align="center">0.701</td>
<td/>
<td valign="top" align="center">&#x02212;0.51 (&#x02212;1.83, 0.80)</td>
<td valign="top" align="center">0.444</td>
<td/>
<td valign="top" align="center">&#x02212;0.54 (&#x02212;1.86, 0.77)</td>
<td valign="top" align="center">0.417</td>
</tr>
<tr>
<td valign="top" align="left">Sex (Male)</td>
<td/>
<td valign="top" align="center">&#x02212;9.65 (&#x02212;12.65, &#x02212;6.65)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td/>
<td valign="top" align="center">&#x02212;9.29 (&#x02212;12.35, &#x02212;6.22)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td/>
<td valign="top" align="center">&#x02212;10.82 (&#x02212;13.88, &#x02212;7.75)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td/>
<td valign="top" align="center">&#x02212;10.63 (&#x02212;13.72, &#x02212;7.54)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SEP</td>
<td/>
<td valign="top" align="center">&#x02212;1.37 (&#x02212;4.33, 1.59)</td>
<td valign="top" align="center">0.364</td>
<td/>
<td valign="top" align="center">&#x02212;1.69 (4.65, 1.27)</td>
<td valign="top" align="center">0.262</td>
<td/>
<td valign="top" align="center">&#x02212;1.45 (&#x02212;4.54, 1.65)</td>
<td valign="top" align="center">0.359</td>
<td/>
<td valign="top" align="center">&#x02212;1.64 (&#x02212;4.75, 1.48)</td>
<td valign="top" align="center">0.302</td>
</tr>
<tr>
<td valign="top" align="left">BMI</td>
<td/>
<td valign="top" align="center">&#x02212;0.04 (&#x02212;0.37, 0.29)</td>
<td valign="top" align="center">0.820</td>
<td/>
<td valign="top" align="center">&#x02212;0.02 (&#x02212;0.35, 0.31)</td>
<td valign="top" align="center">0.910</td>
<td/>
<td valign="top" align="center">0.06 (&#x02212;0.30, 0.41)</td>
<td valign="top" align="center">0.757</td>
<td/>
<td valign="top" align="center">0.09 (&#x02212;0.27, 0.45)</td>
<td valign="top" align="center">0.626</td>
</tr>
<tr>
<td valign="top" align="left">K<sup>&#x0002B;</sup></td>
<td/>
<td valign="top" align="center">&#x02212;5.70 (&#x02212;10.23, &#x02212;1.18)</td>
<td valign="top" align="center"><bold>0.014</bold></td>
<td/>
<td valign="top" align="center">&#x02212;5.96 (&#x02212;10.48, &#x02212;1.43)</td>
<td valign="top" align="center"><bold>0.010</bold></td>
<td/>
<td valign="top" align="center">&#x02212;4.90 (&#x02212;9.61, &#x02212;0.19)</td>
<td valign="top" align="center"><bold>0.042</bold></td>
<td/>
<td valign="top" align="center">&#x02212;4.82 (&#x02212;9.53, &#x02212;0.10)</td>
<td valign="top" align="center"><bold>0.045</bold></td>
</tr>
<tr>
<td valign="top" align="left">LV mass</td>
<td/>
<td valign="top" align="center">0.04 (0.02, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td/>
<td valign="top" align="center">0.04 (0.02, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td/>
<td valign="top" align="center">0.04 (0.03, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td/>
<td valign="top" align="center">0.04 (0.03, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Heart Disease</td>
<td/>
<td valign="top" align="center">2.29 (&#x02212;1.20, 5.77)</td>
<td valign="top" align="center">0.198</td>
<td/>
<td valign="top" align="center">2.01 (&#x02212;1.48, 5.50)</td>
<td valign="top" align="center">0.260</td>
<td/>
<td valign="top" align="center">1.69 (&#x02212;1.96, 5.33)</td>
<td valign="top" align="center">0.364</td>
<td/>
<td valign="top" align="center">1.44 (&#x02212;2.24, 5.12)</td>
<td valign="top" align="center">0.443</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td/>
<td valign="top" align="center">0.954 (&#x02212;1.84, 3.75)</td>
<td valign="top" align="center">0.503</td>
<td/>
<td valign="top" align="center">0.86 (&#x02212;1.94, 3.66)</td>
<td valign="top" align="center">0.548</td>
<td/>
<td valign="top" align="center">1.22 (&#x02212;1.66, 4.10)</td>
<td valign="top" align="center">0.405</td>
<td/>
<td valign="top" align="center">1.40 (&#x02212;1.49, 4.28)</td>
<td valign="top" align="center">0.343</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>Analyses are by generalized linear models after centering for age. Significant p-values are highlighted in bold</italic>.</p>
<p><italic>bpm, beats per minute; CI, confidence interval; &#x003B2;-coefficient, regression coefficient. BMI, body mass index; DBP, diastolic blood pressure; ECG, electrocardiography; EF, ejection fraction; HbA1c, glycated hemoglobin; HDL, high density lipoprotein; IGF-I, insulin-like growth factor-I; IGF-II, insulin-like growth factor-II; IGFBP-3, insulin-like growth factor binding protein 3; LDL, low density lipoprotein; LV, left ventricular; MAP, mean arterial pressure; SBP, systolic blood pressure; SEP, socio-economic position; QTc, corrected QT interval using Hodges&#x00027; formula</italic>.</p>
</table-wrap-foot>
</table-wrap>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p>Multivariable regression (complete case analysis) for IGF-I at 60-64y, IGF-I/BP3 molar ratio at 60-64y, &#x00394;IGF-I, &#x00394;IGF-I/BP3 and exposures of interest with QTc.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th/>
<th valign="top" align="center"><bold>Model 1</bold></th>
<th/>
<th valign="top" align="center"><bold>Model 2</bold></th>
<th/>
<th valign="top" align="center"><bold>Model 3</bold></th>
<th/>
<th valign="top" align="center"><bold>Model 4</bold></th>
<th/>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold>&#x003B2;-coefficient</bold><break/><bold>[95% CI]</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-Value</bold></th>
<th valign="top" align="center"><bold>&#x003B2;-coefficient</bold><break/><bold>[95% CI]</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-Value</bold></th>
<th valign="top" align="center"><bold>&#x003B2;-coefficient</bold><break/><bold>[95% CI]</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-Value</bold></th>
<th valign="top" align="center"><bold>&#x003B2;-coefficient</bold><break/><bold>[95% CI]</bold></th>
<th valign="top" align="center"><bold><italic>p</italic>-Value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic><bold>IGF-I 60-64y</bold></italic></td>
<td valign="top" align="center">&#x02212;0.27 (&#x02212;0.42, &#x02212;0.13)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;0.27 (&#x02212;0.88, 0.99)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;0.25 (&#x02212;0.39, &#x02212;0.11)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02013; 0.23 (&#x02013; 0.40, &#x02013; 0.06)</td>
<td valign="top" align="center"><bold>0.008</bold></td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">0.02 (&#x02212;0.92, 0.95)</td>
<td valign="top" align="center">0.975</td>
<td valign="top" align="center">0.05 (&#x02212;0.41, &#x02212;0.12)</td>
<td valign="top" align="center">0.909</td>
<td valign="top" align="center">&#x02212;0.13 (&#x02212;1.07, 0.79)</td>
<td valign="top" align="center">0.776</td>
<td valign="top" align="center">&#x02013; 0.36 (&#x02013; 1.56, 0.83)</td>
<td valign="top" align="center">0.553</td>
</tr>
<tr>
<td valign="top" align="left">Sex (male)</td>
<td valign="top" align="center">&#x02212;5.00 (&#x02212;7.22, &#x02212;2.79)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;5.04 (&#x02212;7.25, &#x02212;2.82)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;4.62 (&#x02212;6.84, &#x02212;2.41)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02013; 9.10 (&#x02013; 12.00, &#x02013; 6.20)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SEP (manual work)</td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;2.73 (&#x02212;5.12, &#x02212;0.34)</td>
<td valign="top" align="center"><bold>0.025</bold></td>
<td valign="top" align="center">&#x02212;2.31 (&#x02212;4.69, 0.06)</td>
<td valign="top" align="center">0.057</td>
<td valign="top" align="center">&#x02013; 1.56 (&#x02013; 4.34, 1.21)</td>
<td valign="top" align="center">0.271</td>
</tr>
<tr>
<td valign="top" align="left">BMI</td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.45 (0.20, 0.69)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02013; 0.007 (&#x02013; 0.33, 0.32)</td>
<td valign="top" align="center">0.969</td>
</tr>
<tr>
<td valign="top" align="left">K<sup>&#x0002B;</sup></td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x02212;6.93 (&#x02212;10.57, &#x02212;3.29)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02013; 5.43 (&#x02013; 9.81, &#x02013; 1.05)</td>
<td valign="top" align="center"><bold>0.016</bold></td>
</tr>
<tr>
<td valign="top" align="left">LV mass</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.04 (0.02, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Heart disease</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">3.27 (&#x02013; 0.04, 6.60)</td>
<td valign="top" align="center">0.054</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.18 (&#x02013; 1.55, 3.91)</td>
<td valign="top" align="center">0.398</td>
</tr>
<tr>
<td valign="top" align="left"><italic><bold>IGF-I/IGF-BP3 ratio 60-64y</bold></italic></td>
<td valign="top" align="center">&#x02212;24.31 (&#x02212;38.28, &#x02212;9.85)</td>
<td valign="top" align="center"><bold>0.001</bold></td>
<td valign="top" align="center">&#x02212;24.82 (&#x02212;38.81, &#x02212;10.35)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;24.97 (&#x02212;38.87, &#x02212;10.60)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;20.32 (&#x02212;35.60, &#x02212;4.36)</td>
<td valign="top" align="center"><bold>0.012</bold></td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">0.10 (&#x02212;0.86, 1.05)</td>
<td valign="top" align="center">0.842</td>
<td valign="top" align="center">0.15 (&#x02013; 0.80, 1.10)</td>
<td valign="top" align="center">0.756</td>
<td valign="top" align="center">&#x02212;0.03 (&#x02212;0.98, 0.91)</td>
<td valign="top" align="center">0.943</td>
<td valign="top" align="center">&#x02212;0.31 (&#x02212;1.51, 0.89)</td>
<td valign="top" align="center">0.616</td>
</tr>
<tr>
<td valign="top" align="left">Sex (male)</td>
<td valign="top" align="center">&#x02212;4.73 (&#x02212;7.00, &#x02212;2.46)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;4.73 (&#x02212;7.00, &#x02212;2.47)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;4.26 (&#x02212;6.52, &#x02212;1.99)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;8.74 (&#x02212;11.71, &#x02212;5.77)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SEP (manual work)</td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;3.03 (&#x02212;5.42, &#x02212;0.64)</td>
<td valign="top" align="center"><bold>0.013</bold></td>
<td valign="top" align="center">&#x02212;2.60 (&#x02212;4.98, &#x02212;0.23)</td>
<td valign="top" align="center"><bold>0.032</bold></td>
<td valign="top" align="center">&#x02212;1.88 (&#x02212;4.66, 0.89)</td>
<td valign="top" align="center">0.183</td>
</tr>
<tr>
<td valign="top" align="left">BMI</td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.46 (0.22, 0.71)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">0.02 (&#x02212;0.31, 0.34)</td>
<td valign="top" align="center">0.914</td>
</tr>
<tr>
<td valign="top" align="left">K<sup>&#x0002B;</sup></td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x02212;7.27 (&#x02212;10.91, &#x02212;3.63)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;5.64 (&#x02212;10.03, &#x02212;1.26)</td>
<td valign="top" align="center"><bold>0.012</bold></td>
</tr>
<tr>
<td valign="top" align="left">LV mass</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.04 (0.02, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Heart disease</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">3.05 (&#x02212;0.27, 6.39)</td>
<td valign="top" align="center">0.073</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.08 (&#x02212;1.65, 3.82)</td>
<td valign="top" align="center">0.438</td>
</tr>
<tr>
<td valign="top" align="left"><italic><bold>&#x00394;</bold> <bold>IGF-I</bold></italic></td>
<td valign="top" align="center">&#x02212;0.04 (&#x02212;0.08, &#x02212;0.002)</td>
<td valign="top" align="center"><bold>0.039</bold></td>
<td valign="top" align="center">&#x02212;0.04 (&#x02212;0.08, &#x02212;0.003)</td>
<td valign="top" align="center"><bold>0.034</bold></td>
<td valign="top" align="center">&#x02212;0.04 (&#x02212;0.08, &#x02212;0.002)</td>
<td valign="top" align="center"><bold>0.039</bold></td>
<td valign="top" align="center">&#x02212;0.05 (&#x02212;0.09, &#x02212;0.003)</td>
<td valign="top" align="center"><bold>0.038</bold></td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">&#x02212;0.268 (&#x02212;1.32, 0.78)</td>
<td valign="top" align="center">0.617</td>
<td valign="top" align="center">&#x02212;0.19 (&#x02212;1.24, 0.86)</td>
<td valign="top" align="center">0.719</td>
<td valign="top" align="center">&#x02212;0.41 (&#x02212;1.46, 0.63)</td>
<td valign="top" align="center">0.439</td>
<td valign="top" align="center">&#x02212;0.73 (&#x02212;2.04, 0.57)</td>
<td valign="top" align="center">0.270</td>
</tr>
<tr>
<td valign="top" align="left">Sex (male)</td>
<td valign="top" align="center">&#x02212;6.02 (&#x02212;8.34, &#x02212;3.70)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;6.04 (&#x02212;8.36, &#x02212;3.73)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;5.67 (&#x02212;7.98, &#x02212;3.36)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;10.20 (&#x02212;13.21, 7.20)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SEP (manual work)</td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;2.51 (&#x02212;5.07, 0.04)</td>
<td valign="top" align="center">0.054</td>
<td valign="top" align="center">&#x02212;2.16 (&#x02212;4.70, 0.37)</td>
<td valign="top" align="center">0.094</td>
<td valign="top" align="center">&#x02212;1.29 (&#x02212;4.23, 1.65)</td>
<td valign="top" align="center">0.390</td>
</tr>
<tr>
<td valign="top" align="left">BMI</td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.55 (0.29, 0.81)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">0.12 (&#x02212;0.24, 0.47)</td>
<td valign="top" align="center">0.519</td>
</tr>
<tr>
<td valign="top" align="left">K<sup>&#x0002B;</sup></td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x02212;6.62 (&#x02212;10.49, &#x02212;2.75)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;4.49 (&#x02212;9.08, 0.09)</td>
<td valign="top" align="center">0.057</td>
</tr>
<tr>
<td valign="top" align="left">LV mass</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.05 (0.03, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Heart disease</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">2.80 (&#x02212;0.71, 6.33)</td>
<td valign="top" align="center">0.120</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.56 (&#x02212;1.28, 4.40)</td>
<td valign="top" align="center">0.283</td>
</tr>
<tr>
<td valign="top" align="left"><italic><bold>&#x00394;</bold> <bold>IGF-I/IGF-BP3 ratio</bold></italic></td>
<td valign="top" align="center">&#x02212;1.90 (&#x02212;3.72, &#x02212;0.03)</td>
<td valign="top" align="center"><bold>0.046</bold></td>
<td valign="top" align="center">&#x02212;2.02 (&#x02212;3.84, &#x02212;0.14)</td>
<td valign="top" align="center"><bold>0.034</bold></td>
<td valign="top" align="center">&#x02212;2.26 (&#x02212;4.06, 0.40)</td>
<td valign="top" align="center"><bold>0.017</bold></td>
<td valign="top" align="center">&#x02212;2.14 (&#x02212;4.13, &#x02212;0.06)</td>
<td valign="top" align="center"><bold>0.042</bold></td>
</tr>
<tr>
<td valign="top" align="left">Age</td>
<td valign="top" align="center">&#x02212;0.37 (&#x02212;1.42, 0.66)</td>
<td valign="top" align="center">0.481</td>
<td valign="top" align="center">&#x02212;0.285 (&#x02212;1.33, 0.76)</td>
<td valign="top" align="center">0.593</td>
<td valign="top" align="center">&#x02212;0.44 (&#x02212;1.48, 0.59)</td>
<td valign="top" align="center">0.399</td>
<td valign="top" align="center">&#x02212;0.78 (&#x02212;2.08, 0.52)</td>
<td valign="top" align="center">0.240</td>
</tr>
<tr>
<td valign="top" align="left">Sex (male)</td>
<td valign="top" align="center">&#x02212;5.98 (&#x02212;8.32, &#x02212;3.64)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;6.00 (&#x02212;8.34, &#x02212;3.66)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;5.61 (&#x02212;7.94, &#x02212;3.28)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">&#x02212;10.02 (&#x02212;13.05, 7.00)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">SEP (manual work)</td>
<td/>
<td/>
<td valign="top" align="center">&#x02212;2.57 (&#x02212;5.15, 0.005)</td>
<td valign="top" align="center">0.051</td>
<td valign="top" align="center">&#x02212;2.22 (&#x02212;4.78, 0.33)</td>
<td valign="top" align="center">0.089</td>
<td valign="top" align="center">&#x02212;1.45 (&#x02212;4.41, 1.50)</td>
<td valign="top" align="center">0.336</td>
</tr>
<tr>
<td valign="top" align="left">BMI</td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.58 (0.31, 0.84)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
<td valign="top" align="center">0.15 (&#x02212;0.21, 0.50)</td>
<td valign="top" align="center">0.415</td>
</tr>
<tr>
<td valign="top" align="left">K<sup>&#x0002B;</sup></td>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x02212;6.55 (&#x02212;10.44, &#x02212;2.66)</td>
<td valign="top" align="center"><bold>0.001</bold></td>
<td valign="top" align="center">&#x02212;4.40 (&#x02212;8.99, 0.195)</td>
<td valign="top" align="center">0.063</td>
</tr>
<tr>
<td valign="top" align="left">LV mass</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.04 (0.03, 0.06)</td>
<td valign="top" align="center"><bold>&#x0003C;0.001</bold></td>
</tr>
<tr>
<td valign="top" align="left">Heart disease</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">2.61 (&#x02212;0.92, 6.17)</td>
<td valign="top" align="center">0.149</td>
</tr>
<tr>
<td valign="top" align="left">Hypertension</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.72 (&#x02212;1.12, 4.57)</td>
<td valign="top" align="center">0.236</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>Analyses are by generalized linear models after centering for age. Significant p-values are highlighted in bold</italic>.</p>
<p><italic>Heart rate was not included in the multivariable model as it was already accounted for by the QTc correction using Hodges&#x00027; formula. Four models were used from left to right in a stepwise manner. Model 1 was centered for age and adjusted for sex, Model 2 was adjusted as for model 1 &#x0002B; SEP, Model 3 was adjusted as for model 2 &#x0002B; clinical covariates (BMI and K<sup>&#x0002B;</sup>) and Model 4 was adjusted as for Model 3 &#x0002B; cardiac covariates (LV mass, heart disease and hypertension)</italic>.</p>
<p><italic>bpm, beats per minute; CI, confidence interval; &#x003B2;-coefficient, regression coefficient. BMI, body mass index; DBP, diastolic blood pressure; ECG, electrocardiography; EF, ejection fraction; HbA1c, glycated hemoglobin; HDL, high density lipoprotein; IGF-I, insulin-like growth factor-I; IGF-II, insulin-like growth factor-II; IGFBP-3, insulin-like growth factor binding protein 3; LDL, low density lipoprotein; LV, left ventricular; MAP, mean arterial pressure; SBP, systolic blood pressure; SEP, socio-economic position; QTc, corrected QT interval using Hodges&#x00027; formula</italic>.</p>
</table-wrap-foot>
</table-wrap>
<p>Based on these multiple imputation models, independent associations with QTc were also confirmed for other previously known covariates: female sex (&#x003B2; 9.65 [6.65, 12.65] <italic>p</italic> &#x0003C; 0.001), increased left ventricular mass (&#x003B2; 0.04 ms/g [0.02, 0.06] <italic>p</italic> &#x0003C; 0.001) and blood potassium levels (&#x003B2; &#x02212;5.70 ms/mmol/L [&#x02212;10.23, &#x02212;1.18] <italic>p</italic> = 0.014).</p></sec>
<sec>
<title>&#x00394;IGFs and &#x00394;IGFBP-3 in Relation to QTc Interval Over a Decade</title>
<p>IGF-I, IGF-II and IGFBP-3 levels decreased with age (all <italic>p</italic> &#x0003C; 0.001; <xref ref-type="fig" rid="F2">Figure 2</xref>) while the IGF-I/IGFBP-3 ratio increased with age (<italic>p</italic> &#x0003C; 0.001; <xref ref-type="fig" rid="F2">Figure 2</xref>).</p>
<fig id="F2" position="float">
<label>Figure 2</label>
<caption><p>Boxplots comparing circulating levels of IGF-I, IGF-II, IGFBP-3 and IGF-I/IGFBP-3 molar ratio at 53 and 60&#x02013;64 years. Whiskers indicate variability outside the third and first quartiles [75th and 25th percentiles] represented as hinges around the median [bold midline]. <italic>p</italic>-Values derived from Mann-Whitney tests. ECG, electrocardiography; IGF-I, insulin-like growth factor 1; IGF-BP3, insulin-like growth factor binding protein 3.</p></caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fcvm-09-863988-g0002.tif"/>
</fig>
<p>On univariate analysis (<xref ref-type="table" rid="T2">Table 2</xref>) a steeper decline in IGF-I, &#x00394;IGF-I, (&#x003B2; &#x02212;0.04 ms/nmol/L/10 years [&#x02212;0.08, &#x02212;0.008], <italic>p</italic> = 0.019) and a shallower rise in &#x00394;IGF-I/IGFBP-3 (&#x003B2; &#x02212;2.44 ms/unit/10 years [&#x02212;4.17, &#x02212;0.67], <italic>p</italic> = 0.007) were associated with a longer QTc. These associations persisted in complete case analysis multivariable models (&#x003B2; &#x02212;0.05 ms/nmol/L/10 years [&#x02212;0.09, &#x02212;0.003], <italic>p</italic> = 0.038 and &#x003B2; &#x02212;2.14 ms/unit/10 years [&#x02212;4.13, &#x02212;0.06], <italic>p</italic> = 0.042) and in fully adjusted imputed models (&#x003B2; &#x02212;0.05 ms/nmol/L/10 years [&#x02212;0.10, &#x02212;0.002], <italic>p</italic> = 0.042 and &#x003B2; &#x02212;2.16 ms/unit/10 years [&#x02212;4.23, &#x02212;0.09], <italic>p</italic> = 0.041). As before, the multiple imputation analyses remained significant for sex, left ventricular mass and blood potassium levels.</p></sec>
<sec>
<title>Sensitivity Analyses</title>
<p>In imputed models, observed associations between IGF-I/IGFBP-3 molar ratio at 60&#x02013;64y and &#x00394;IGF-I/IGFBP-3 ratio with QTc, were only slightly attenuated after removing the 273 participants with known cardiovascular disease (respectively: &#x003B2; &#x02212;17.60 ms/unit [&#x02212;34.41, &#x02212;0.80], <italic>p</italic> = 0.040 and &#x003B2; &#x02212;2.35 ms/unit/10 years [&#x02212;4.44, &#x02212;0.24], <italic>p</italic> = 0.029, <xref ref-type="supplementary-material" rid="SM1">Supplementary Table 3</xref>).</p></sec></sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>In a UK-based sample of older persons aged 60&#x02013;64 years, declining serum levels and bioavailability of IGF-I (previously known as Somatomedin C) associated with QTcprolongation, a well-established risk factor for sudden death, independent of sex, SEP, BMI, LV mass, heart disease and hypertension.</p>
<p>In several studies it has been shown that reduced levels of IGF-I were associated with increased risk of cardiovascular disease (<xref ref-type="bibr" rid="B44">44</xref>, <xref ref-type="bibr" rid="B45">45</xref>). IGF-I is thought to protect cardiac myocytes from arrhythmogenesis and apoptosis by activating the PI3-K/Akt cell survival intracellular signaling (<xref ref-type="bibr" rid="B21">21</xref>), although the exact mechanism remains to be fully elucidated, with a possibility that channel transcription might be affected. The PI3-K pathway activates the serine/threonine protein kinase Akt, and Protein Kinase C, promoting cardiovascular homeostasis, neuroprotection, survival, gene expression, and insulin activity (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B46">46</xref>). The rapid delayed rectifier potassium channels that influence cardiac repolarization in cardiomyocytes are regulated by PI3-K (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B47">47</xref>). These channels (I<sub>Ks</sub>, I<sub>Kr</sub> and the atrial specific I<sub>Kur</sub>) conduct outward potassium currents during the plateau phase of the action potential (<xref ref-type="bibr" rid="B48">48</xref>). Mutations in the genes encoding delayed rectifiers disrupt normal cardiac repolarization and lead to various cardiac rhythm disorders, including congenital long QT syndrome. Late sodium currents, I<sub>NaL</sub>, were shown to be activated by another a downward pathway of PI3-K, the serum- and glucocorticoid-regulated kinase (SGK), which phosphorylates the sodium channels on the cardiomyocyte surface. GSK was shown to phosphorylates neural precursor cell expressed developmentally down-regulated protein 4 (NEED4), blocking the ubiquitination of the sodium channel (<xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B50">50</xref>). These two channels, I<sub>Kr</sub> and I<sub>Na</sub>, were thought to finely interact to maintain a correct ventricular repolarization (<xref ref-type="bibr" rid="B51">51</xref>). Reduced IGF-I binding to its receptors on cardiomyocytes, has been shown to decrease the activation of the PI3-K/Akt pathway in animal models (<xref ref-type="bibr" rid="B22">22</xref>) thus prolonging the action potential duration and also the QTc. One factor that is thought to mediate the electrophysiological effect of IGF-I is nuclear factor erythroid 2-related factor 2 (Nrf2), which normally induces the transcription of cytoprotective enzymes involved in antioxidative pathways (<xref ref-type="bibr" rid="B52">52</xref>, <xref ref-type="bibr" rid="B53">53</xref>) that have the ability to suppress ventricular arrhythmias. Evidence suggests that IGF-I regulates the levels of Nrf2 expression and modulates its transcriptional activity via the PI3K/Akt pathway (<xref ref-type="bibr" rid="B54">54</xref>). Based on the results from our current study, it is plausible therefore, that in older persons, declining levels and bioavailability of IGF-I reduce expression of Nrf2, prolonging QTc, and increasing the propensity for malignant arrhythmias.</p>
<p>IGF-I and IGF-II are mainly produced by the liver under the influence of growth hormone and nutrition (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B55">55</xref>, <xref ref-type="bibr" rid="B56">56</xref>). They have a similar structure to insulin with a similar direct effect on the body&#x00027;s glucose metabolism (<xref ref-type="bibr" rid="B57">57</xref>, <xref ref-type="bibr" rid="B58">58</xref>), but are found in much higher concentrations in the blood where they are bound to binding proteins (mainly to isoform IGFBP-3), also produced by the liver. Binding to IGFBP-3 increases its half-life and modulates receptor binding. IGF-I fulfills several important functions in the human body by reaching several targets, where both insulin and IGF-1 receptors are found. An imbalance in serum IGF-I levels has been associated with a variety of negative effects, in several body systems, including obesity, diabetes and atherosclerosis. Reduced levels of IGF-I increase risk of hypertension, inflammation and endothelial dysfunction as normal IGF-I levels were shown to be protective, stimulating the release of nitric oxide, a vasodilator, and promoting cell proliferation and differentiation (<xref ref-type="bibr" rid="B46">46</xref>, <xref ref-type="bibr" rid="B59">59</xref>). Quantitatively, IGF-II is the predominant circulating IGF, present in adults at a concentration up to three times that of IGF-I (as noted in our cohort). In mammals, IGF-I mediates the growth promoting effects of growth hormone during postnatal life and throughout adulthood. It influences cardiomyocytes and the cardiac action potential as described above, but IGF-II is more involved in placental and fetal growth (including cardiac development) in utero (<xref ref-type="bibr" rid="B60">60</xref>), is less growth hormone dependent than IGF-I (<xref ref-type="bibr" rid="B61">61</xref>), and is not known to influence the cardiac action potential in mature cardiomyocytes. Indeed, we found no statistically significant association between circulating levels of IGF-II and QTc interval at either time point. As IGF-I fulfills important somatic growth function it reaches its highest levels during teenage years, with levels subsequently decreasing with age (<xref ref-type="bibr" rid="B16">16</xref>, <xref ref-type="bibr" rid="B17">17</xref>) in a highly variable and individual process that is related to fat mass (<xref ref-type="bibr" rid="B16">16</xref>), sex, diet and hormonal status. Among its various functions, IGF-I protects against inflammation, hypertension, endothelial and &#x003B2;-cell dysfunction (<xref ref-type="bibr" rid="B46">46</xref>, <xref ref-type="bibr" rid="B59">59</xref>, <xref ref-type="bibr" rid="B62">62</xref>), inhibits growth hormone hypersecretion and suppresses insulin secretion whilst enhancing insulin&#x00027;s action (<xref ref-type="bibr" rid="B63">63</xref>). An imbalance in serum IGF-I levels has been associated with obesity, diabetes and atherosclerosis (<xref ref-type="bibr" rid="B46">46</xref>, <xref ref-type="bibr" rid="B59">59</xref>).</p>
<p>The six IGF-binding proteins (IGFBP-1-6) have both IGF-dependent and independent functions (<xref ref-type="bibr" rid="B64">64</xref>, <xref ref-type="bibr" rid="B65">65</xref>). IGFBP-3 is the most abundant of the six IGFBPs in the circulation. Low IGFBP-3 levels have been associated with adverse cardiovascular effects, increased vascular disease and higher risk of coronary events (<xref ref-type="bibr" rid="B66">66</xref>). IGFBPs serve not only to transport IGFs in the circulation but also to prolong their half-lives, modulate their tissue specificity, and to either potentiate or neutralize their biological actions at tissue level (<xref ref-type="bibr" rid="B65">65</xref>). Measuring free (unbound) IGF-I remains a challenge (<xref ref-type="bibr" rid="B52">52</xref>) and total measured serum IGF-I is not tantamount to bioavailable IGF-I (<xref ref-type="bibr" rid="B33">33</xref>): almost all circulating IGF-I is bound to IGFBPs leaving &#x0003C;1% of IGF-I in a free form bioavailable for receptor-binding (<xref ref-type="bibr" rid="B47">47</xref>). The molar ratio of total IGF-I to IGFBP-3 is widely used as the proxy for bioavailable IGF-I (<xref ref-type="bibr" rid="B67">67</xref>).</p>
<p>Our study examined whether the differential decline in IGFs, IGFBP-3 or IGF-I/IGFBP-3 ratio over a decade (53 to 60&#x02013;64 years) put some older persons at higher risk of QTc prolongation. Results showed that adults who experienced a steeper decline in IGF-I over a decade of later life were at higher risk of QTc prolongation. With age, levels of IGFBP-3 decline more steeply than IGF-I which is why the IGF-I/IGFBP-3 ratio (and therefore IGF-I bioavailability) appears to increase (<xref ref-type="bibr" rid="B32">32</xref>) (<xref ref-type="fig" rid="F2">Figure 2</xref>). We show that older persons whose molar ratios increased least over a decade&#x02014;implying less bioavailable IGF-I overall&#x02014;had prolonged QTc compared to those who had higher IGF-I bioavailability. This aligns with other recently published data showing how low levels of IGFBP-3 increased the risk of cardiovascular disease and mortality (<xref ref-type="bibr" rid="B18">18</xref>) and how low IGF-I/IGFBP-3 ratios increased the risk of metabolic syndrome and insulin resistance (<xref ref-type="bibr" rid="B68">68</xref>). The fact that after removing persons with cardiovascular disease in the sensitivity analysis, only the 60&#x02013;64y IGF-I/IGFBP-3 molar ratio and &#x00394;IGF-I/IGFBP-3 ratio retained significant association with QTc, adds credence to the notion that it is the free, bioavailable IGF-I which most strongly determines the electrophysiological effect observed. Therefore, the IGF-I/IGFBP-3 ratio has to maintained at safe levels to avoid adverse metabolic, cardiovascular and neoplastic effects (<xref ref-type="bibr" rid="B69">69</xref>).</p>
<p>Our finding, that low levels of IGF-I relate to QTc prolongation and therefore higher sudden cardiac death risk, fits with several other known adverse cardiovascular effects of IGF-I deficiency, that include accelerated cardiovascular aging, reduced cardiac contractility, hypertrophy, hypertension, coronary disease and even atrial fibrillation (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B53">53</xref>, <xref ref-type="bibr" rid="B70">70</xref>). Conversely, normal IGF-I levels appear to be cardioprotective, by stimulating the release of vasodilatory nitric oxide, and by promoting cell proliferation and differentiation (<xref ref-type="bibr" rid="B46">46</xref>, <xref ref-type="bibr" rid="B59">59</xref>). In mice IGF-I receptor deficiency has been associated with cardiomyopathy and heart failure (<xref ref-type="bibr" rid="B53">53</xref>).</p>
<p>Previous works that failed to account for confounders, have observed an association between diabetes or insulin resistance with QTc prolongation (<xref ref-type="bibr" rid="B71">71</xref>&#x02013;<xref ref-type="bibr" rid="B75">75</xref>), but we found no association between fasting blood glucose levels or HbA1c with QTc at age 60-64. The mechanism of QTc prolongation in insulin resistance is still unclear but it is known that insulin may increase the transmembrane potential of cardiomyocytes by activating the electrogenic Na<sup>&#x0002B;</sup>/K<sup>&#x0002B;</sup>-ATPase leading to hyperpolarization and therefore QT prolongation (<xref ref-type="bibr" rid="B76">76</xref>).</p>
<p>We found an association between low blood potassium levels and QTc prolongation, as anticipated. Important cardiomyocyte-specific mechanisms are the main determinants of hypokalemia-induced QT prolongation (<xref ref-type="bibr" rid="B77">77</xref>) in addition to altering autonomic nervous system activity (<xref ref-type="bibr" rid="B78">78</xref>, <xref ref-type="bibr" rid="B79">79</xref>). Potassium deficiency, as shown experimentally in a legacy study on rats (<xref ref-type="bibr" rid="B80">80</xref>), also reduces IGF-I, so it potentially confounds the relationship between IGF-I and QTc. Several previous studies (<xref ref-type="bibr" rid="B81">81</xref>&#x02013;<xref ref-type="bibr" rid="B83">83</xref>) explored an association between increased LV mass and prolonged QTc, supporting our evidence, that LV mass is also an important determinant of QTc.</p>
<sec>
<title>Limitations</title>
<p>As with most epidemiological studies, the main limitation is unmeasured or residual confounding as this precludes causal inferences. The extent of missing data in our study (<xref ref-type="supplementary-material" rid="SM1">Supplementary Tables 1, 2</xref>) was small and we go on to show that key associations between biomarkers and QTc persisted after multiple imputation. Multiple imputation, however, cannot account for sample selection. The inclusion of British people born during the same week in 1946, leads to issues with external validity as the findings may not be applicable to non-British populations.</p>
<p>Earlier measurements of IGFs and IGFBP-3 (pre-53y), as well as measurements at shorter time intervals could have helped detect temporal trends better in this cohort. Levels of other IGFBPs that may have more direct effects on cardiac function were not measured (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B84">84</xref>). The broader metabolic effects of IGF-I linked to other metabolic markers such as leptin, insulin or glucose, could not be explored over the decade as measurements of these additional blood markers were not available at the age of 53y. Several methods for QT correction exist and although the Bazett&#x00027;s formula remains one of the most widely used methods, it is known to overcorrect the QT interval (<xref ref-type="bibr" rid="B85">85</xref>, <xref ref-type="bibr" rid="B86">86</xref>). Other correction methods (<xref ref-type="bibr" rid="B86">86</xref>, <xref ref-type="bibr" rid="B87">87</xref>), including Hodges&#x00027; formula used here, have been shown to be better (<xref ref-type="bibr" rid="B86">86</xref>). Comparisons between the various QTc correction methods was beyond the scope of this study, yet we go on to show in the sensitivity analysis (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table 4</xref>) that key biomarker associations with QTc persisted after additional adjustment for heart rate in the multivariable models.</p></sec></sec>
<sec sec-type="conclusions" id="s5">
<title>Conclusion</title>
<p>In a large older-age population-based cohort, declining levels and bioavailability of IGF-I associate with prolonged QTc interval. As QTc prolongation is known to be associated with increased risk for sudden death even in apparently healthy people, further work is needed to understand and preserve the potentially anti-arrhythmic effects of IGF-I in older age.</p></sec>
<sec sec-type="data-availability" id="s6">
<title>Data Availability Statement</title>
<p>NSHD data is available through the Medical Research Council Skylark website (<ext-link ext-link-type="uri" xlink:href="https://skylark.ucl.ac.uk/Skylark">https://skylark.ucl.ac.uk/Skylark</ext-link>) and full details on the archived data is available at: <ext-link ext-link-type="uri" xlink:href="https://www.nshd.mrc.ac.uk/data">https://www.nshd.mrc.ac.uk/data</ext-link>.</p></sec>
<sec id="s7">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by Greater Manchester Local Research Ethics Committee and the Scotland Research Ethics Committee. The patients participants provided their written informed consent to participate in this study.</p></sec>
<sec id="s8">
<title>Author Contributions</title>
<p>CC and GC conceived of the study. CC wrote the manuscript and analyzed the data. JM, JH, NC, and AH provided expert review of the manuscript. All authors contributed to the article and approved the submitted version.</p></sec>
<sec sec-type="funding-information" id="s9">
<title>Funding</title>
<p>GC and JM are supported by the Barts Charity HeartOME grant (MGU0427). GC was supported by British Heart Foundation (MyoFit46 Special Programme Grant SP/20/2/34841) and by the NIHR UCL Hospitals Biomedical Research Center. The NSHD cohort was funded by the UK MRC (program codes MC_UU_12019/1; MC_UU_12019/4; MC_UU_12019/5). JM is directly and indirectly supported by the UCL Hospitals NIHR BRC and Biomedical Research Unit at Barts Hospital, respectively. AH receives support from the British Heart Foundation, the Economic and Social Research Council (ESRC), the Horizon 2020 Framework Programme of the European Union, the National Institute on Aging, the National Institute for Health Research University College London Hospitals Biomedical Research Center and the UK MRC.</p>
</sec>
<sec sec-type="COI-statement" id="conf1">
<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="disclaimer" id="s10">
<title>Publisher&#x00027;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>
</body>
<back>
<ack><p>The authors would like to thank NSHD members for their participation and continuous engagement with follow-up and all past and present NSHD scientific and data collection teams. The authors are also grateful to Imran Shah and Andrew Wong at the MRC Unit for Lifelong Health and Aging at UCL for his assistance with NSHD data access.</p>
</ack><sec sec-type="supplementary-material" id="s11">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fcvm.2022.863988/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fcvm.2022.863988/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.pdf" id="SM1" mimetype="application/pdf" xmlns:xlink="http://www.w3.org/1999/xlink"/></sec>
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