Genetic risk for Alzheimer’s disease, and differential trajectories in circulating blood biomarkers in UK Biobank (n=17,817)

Genetic risk for Alzheimer’s disease, and differential trajectories in circulating blood biomarkers in UK Biobank (n=17,817) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genetic risk for Alzheimer’s disease, and differential trajectories in circulating blood biomarkers in UK Biobank (n=17,817) Donald Lyall, Oluwatobi Oni, Amy McKie, Jack Beazer, Angelina Kancheva, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6802545/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding pathways from genetic variation to cognitive impairment is critical for dementia prevention, risk stratification and the development of treatments. While genetic risk factors for dementia are known to associate with cross-sectional differences in biomarkers (e.g. lipids) in healthy people, potential influence over longitudinal trajectories is not understood. We leveraged genetic, general health and two-wave biomarker data from n = 17,817 UK Biobank participants. The outcomes were change in 26 common circulating blood biomarkers including inflammatory, cardiometabolic and lipid families. The presence of apolipoprotein ( APOE ) e4 ‘risk’ and e2 ‘protective’ alleles were tested separately versus ‘neutral’ e3e3 genotype, as were associations of non- APOE polygenic risk for Alzheimer’s disease. Biomarker change values were corrected for baseline levels, age, deprivation, sex, timepoint interval, smoking history, medication history, deprivation, genotyping chip and 10 genetic principal components (fully-adjusted). The average interval between assessments was 4.30 years (standard deviation; SD = 0.92). For e4 (versus e3e3), four associations were significant: accelerated change in total cholesterol, apolipoprotein b (ApoB) and low-density lipoprotein (LDL) each in the direction of poorer health (standardized β range = 0.021 SDs to 0.036 more change relative to e3e3), and c-reactive protein protectively (β = -0.059; all P < 0.001). For e2 allele presence, there were three significant associations: change in ApoB, total cholesterol and LDL in protective directions (β range = -0.057 to -0.090). There were no APOE genotypic interactions with baseline age, sex, or medication history, nor significant findings associated with non- APOE (Alzheimer’s disease) polygenic risk. APOE e genotype significantly modifies particularly lipid trajectories across time – most strongly ApoB levels. This adds nuance to lipids as a dementia risk factor, and, clinically, suggests more frequent lipid assessments in e4 carriers in that context. Our findings provide a plausible partial biological explanation for APOE’s progressive influence on neurocognitive health. Health sciences/Medical research/Genetics research Biological sciences/Genetics/Genetic markers Health sciences/Risk factors UK Biobank biomarkers dementia longitudinal Apolipoprotein b Figures Figure 1 Introduction Understanding the pathways from human genetic differences towards ultimate disease manifestation is a scientific and clinical priority because this may lead to targetable, preventive approaches. Apolipoprotein (APOE) e genotype is the largest risk factor for Alzheimer’s disease (AD) dementia after increasing age, and the e4 allele is associated with earlier age at disease onset and accelerated cognitive ageing generally[ 1 ]. Around 42% of AD can be statistically attributed to APOE e genotype[ 2 ] (via population attributable fraction). APOE is highly pleiotropic, influencing multiple biological functions partly via a dominant role in lipid metabolism[ 3 ]. Data from cohort studies demonstrates that APOE genotype’s influence on brain health increases over time - having little to no influence in early/mid-life, but significantly so in later life[ 4 – 6 ]. Broadly, it seems to influence change in relative brain health, rather than baseline levels[ 7 ]. This ‘change influence’ may manifest in other health phenotypes across the lifespan. Investigating the potential influence of APOE genotype, a major dementia genetic risk factor, on change across timepoints in circulating blood biomarkers which are indicative of multiple aspects of health, could provide a greater understanding of the seemingly time-dependent mechanisms linking APOE to dementia and age-related cognitive decline. We have previously demonstrated widespread associations between APOE genotype and cross-sectional blood biomarker levels[ 8 ]. In that study of N ~ 396k UK Biobank (UKB) participants, the largest associations were found with total cholesterol (0.13 standard deviations/SDs difference per e4 allele), ApoB (0.20 SDs), and LDL (0.15 SDs), with e2 genotype associations generally in the correspondingly opposite, protective direction. This is important because it shows influence of AD-related genetic influence on known potential dementia risk factors like LDL[ 9 ], possibly years before disease onset. In genetic-biomarker studies, cross-sectional associations may reflect a mixture of variance in baseline levels of the biomarker and the degree of change in it across time. As such, longitudinal data are often considered additionally informative and a more stringent test for potential causal hypotheses. Here, in ~ 18,000 participants with two-wave longitudinal biomarker data from UKB, we tested associations between APOE e2 ‘protective’ and e4 ‘risk’ genotypes, and ~ 4-year change in 26 common clinical measurements associated with health. Methods Participants UKB is a prospective general population cohort, where approximately 502,000 participants attended one of 22 assessment centres in Scotland, England and Wales, between 2006 and 2010[ 10 ]. Participants were aged 40–70 years at baseline. Approximately 4 years later, a sub-sample of n = 20,339 underwent repeat assessment, in one centre (Stockport). Biomarkers were assessed at both visits. This research was completed using UKB project #17689. Ethics Participants provided full informed consent to participate in UK Biobank. This study was covered by the generic ethical approval for UK Biobank studies from the NHS National Research Ethics Service (approval letter dated 17th June 2011, Ref 11/NW/0382). Genotyping UK Biobank participants were genotyped using Applied Biosystems UK BiLEVE Axiom array by Affymetrix and Applied Biosystems UK Biobank Axiom Array which share 95% marker content [ 11 ]. APOE e status was based on two single nucleotide polymorphisms (SNPs): rs7412 and rs429358. Stringent quality control and processing were applied to the data, detailed at http://www.ukbiobank.ac.uk/scientists-3/genetic-data and http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf . Genetic principal components were calculated by UK Biobank. The polygenic risk score for AD was calculated using an infinitesimal model with LDPred software[ 12 ]. A total of 6,578,321 SNPs (including imputed single nucleotide polymorphisms [SNPs] at a minimum 80% confidence) with varying effect estimates associated with late-onset AD, from a previous genome-wide association study, were included to calculate weighted risk scores[ 13 , 14 ]. Biomarker processing We examined 26 circulating blood biomarker phenotypes which are in common clinical use and were selected on the basis of being established risk factors for certain conditions, diagnostic tools, or to examine health in otherwise difficult-to-assess phenotypes[ 15 ]. These are listed in Table 1 , with additional information on the UKB website: https://www.ukbiobank.ac.uk/media/oiudpjqa/bcm023_ukb_biomarker_panel_website_v1-0-aug-2015-edit-2018.pdf . Biomarker levels were analysed in UKB from serum and packed red blood cell samples obtained from all UKB participants at both visits [ 16 ]. Of all the measures on the panel, we did not assess change in oestrodiol or rheumatoid factor because large numbers (> 80%) of participants had missing data, likely due to ‘true’ values below the lowest measurable level[ 15 ]. Stringent quality controls were applied to the assays used measure biomarker levels. Details of biomarker quality control, instrumentation and analysis methods are available at: https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/biomarker_issues.pdf , https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf , http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/haematology.pdf Covariates Age was calculated by UKB based on date of birth versus date of assessment. Sex and smoking history were self-reported, the latter as current, past or never. We collated past and current smokers into ‘ever’ (vs. never). Participants separately self-reported current medication for lowering lipid, insulin, or blood pressure levels and this was binarized (yes; no). Townsend scores indicate area-based deprivation based on participant postcode, where higher scores reflect greater deprivation. Quality controlling We excluded participants with non-white British ancestry due to observed APOE genotypic frequency differences between ethnicities[ 17 ], sex mismatch (self-report versus genetic), chromosomal aneuploidy, excessive heterozygosity and genotype missing rate > 10%. In instances of 1st degree relatedness we removed one participant at random. We did not include the minority of participants with e2/e4 (< 1%) genotype in effect contrasts because this included both potentially protective and risk alleles. We removed participants with < 100 days assessment interval (n = 2). Primary analyses We firstly tested for absolute differences in biomarker values across waves, using Students and Mann-Whitney (non-parametric equivalent) T-tests. For uncorrected change statistics across the two timepoints, Cohen’s D standardized effect sizes are provided (where 0.2 reflects a small effect size and 0.5 is considered medium), and matched rank biserial correlation statistics for non-parametric equivalents. We tested descriptive inter-correlations between all timepoint 1 biomarkers with unadjusted Pearson correlations. We subsequently used linear regression to test for associations between possession of the APOE e4 and e2 alleles versus e3e3 neutral genotype, and change in biomarkers. The key outcome was timepoint 2 biomarker values adjusted for timepoint 1 values (i.e. residualized change)[ 18 ]. We adjusted for covariates of respective baseline biomarker values, age at baseline, sex, Townsend deprivation index, ever-smoking history, timepoint interval, medication yes/no, genotypic chip, and 10 genetic principal components (PCs) for ancestral stratification. Analyses were run in two models: partially-adjusted (age; sex; baseline biomarker value; timepoint interval in days; chip and 10 PCs) and fully-adjusted (all covariates). We conservatively considered P ≤ 0.001 significant, and report unstandardized parameter estimates. For significant findings we also report standardized betas (i.e. on the per-SD scale) for ease of interpretation, in the text. Secondary analyses We subsequently tested for associations of non- APOE polygenic risk for AD[ 13 , 14 ] with biomarker change. This was based on a non-UKB genome-wide association study (GWAS). Polygenic risk scores were standardised to Z scores, i.e. mean = 0, SD = 1 such that higher scores reflect increased risk. These analyses controlled for individual APOE e genotypic status, as a factor. We tested for interactions between APOE genotypes with male/female sex, and separately baseline age, on outcomes. Statistical packages were PLINK 1.90 (for APOE and PGR derivation), Stata 18 for analyses and JASP 0.19.1 for visualization. Results In total, N = 17,817 participants had APOE e genotypic and two-wave biomarker data after exclusions. The average age at baseline was 57.39 (SD = 7.33), and 9,081 (51%) participants were female. The average interval between baseline and repeat assessments was 4.30 years (SD = 0.92, range = 2.11 to 7.0). Paired-sample T-tests showed significant uncorrected changes in all biomarkers between timepoint 1 and timepoint 2 (Supplementary Table 1), with standardized effect sizes typically around 0.1 (i.e. ‘small’ effect) based on Cohen’s D standardized metrics. Supplementary Table 2 shows inter-correlations between the different biomarkers, of which the majority were significant at P < 0.001. APOE e genotypic frequencies are shown in Supplementary Table 3. [Insert Table 1 here] APOE genotype and change in biomarker values Partially and fully-adjusted models generally had very similar results. For e4 allele presence (versus e3e3), four change associations (out of 26) were significant at P ≤ 0.001, and these are shown in Table 1 . These were: CRP (standardized β = -0.059, P < 0.001 relative to e3e3), ApoB (standardized β = 0.036, P < 0.001), total cholesterol (standardized β = 0.021, P = 0.001) and LDL (standardized β = 0.025, P = 0.001). Possession of APOE e4 was associated with change in a protective direction for CRP, and deleteriously (i.e. change in an unhealthy direction) for ApoB, total cholesterol, and LDL. This means that that carriers of an APOE e4 allele showed above-average change in lipids, in the direction of poorer health, relative to neutral e3/e3 genotype group. [Insert Table 2 here] For e2 presence (versus neutral e3e3), there were three significant associations out of 26. These were change in ApoB (standardized β = -0.090, P < 0.001 relative to e3e3), total cholesterol (standardized β = -0.057, P < 0.001), and LDL (standardized β = -0.074, P < 0.001). APOE e2 was associated in protective directions for ApoB, total cholesterol and LDL (Table 2 ). The strongest, most consistent APOE genotypic association was found for change in ApoB. This followed an e2/e3/e4 dose-response risk gradient of: e2e2; e2e3; e2e4; e3e3; e3e4; e4e4 (parameter estimate = 0.022, P < 0.001 average increase per allelic difference). Estimated marginal mean change values are shown in Fig. 1 , and trajectories for ApoB between timepoints 1 and 2 stratified by APOE genotype, are shown in Supplementary Fig. 1. Full model results for ApoB change are shown in Supplementary Table 4, where the largest non- APOE genotypic associations are male versus female sex (parameter estimate = -0.044, 95% CI = -0.050 to -0.037, P < 0.001) and medication use (parameter estimate = -0.037, 95% CI = -0.044 to -0.029, P < 0.001). This means that on average the ApoB levels of males, and participants on medication, typically got lower across timepoints (female estimated marginal mean change = 0.020 g/L versus male − 0.024 g/L; no-medication average change = 0.009 g/L; on-medication average change = -0.028). [Insert Fig. 1 here] Non-APOE polygenic risk for Alzheimer’s disease, and biomarker change values There were no significant associations between polygenic risk for AD and biomarker change values, controlling for APOE e genotype (Supplementary Table 5). Interactions between APOE genotype with sex, age, medication history, and sensitivity analyses There was no evidence of interaction between APOE genotype and sex on biomarker changes at our conservative significance threshold (lowest P = 0.010, for glucose/e4 presence), nor medication (lowest P = 0.009 for e2 genotype versus e3e3, yes/no medication and aspartate). There were no significant interactions with age (lowest P-value = ApoB and e2, interaction p = 0.007). Correspondingly, the findings were very similar when we re-ran associations in older participants aged 65 years and above. Discussion APOE e genotype is a moderator of age-related cognitive decline including risk of dementia[ 19 ]. Understanding its influence on premorbid phenotypes, in people with no evidence of dementia, may inform part of the pathway from genetic variation to poorer later-life brain health[ 8 ]. Having previously shown widespread cross-sectional associations between APOE genotype with multiple biomarkers (e.g. LDL, ApoB, HbA1c) in N ~ 396k[ 8 ], here we extend that. APOE e genotype significantly modifies longitudinal trajectories in biomarker levels across approximately 4 years. This is most prominent for lipids, e.g. ApoB. Relative to neutral e3e3 genotype, ‘risk’ e4 carriers tended to have less healthy trajectories, and e2 ‘protective’ carriers tended to have protective trajectories. These associations generally survived correction for potential confounders e.g. deprivation. These findings were not modified by age at time of assessment, sex, or medication history, and were evident in the cohort including people from late middle-age, i.e. not necessarily at a typical age of dementia onset. Our findings add nuance to the proposed causal association between lipids and dementia[ 20 ], namely that APOE genetic variation contributes to differences in lipid levels[ 21 ] increasingly across time[ 22 ]. APOE genotype seems to primarily associate particularly with poorer older-age rather than early/mid-life cognitive health[ 23 ], suggesting progressive influence over the lifespan. Association with baseline but also longitudinal change in lipids may feasibly mediate some of APOE genotype age-dependent association with brain health. The largest APOE change association was found for ApoB, and this broadly followed a ‘risk’ gradient associated with APOE genotype and AD: namely e2/e2 carriers showed the healthiest trajectory, e3 neutral and e4 carriers generally worse across time. E4 homozygotes did not perfectly continue this linear trajectory as would be expected, and this may reflect some aspect of power or attrition/survivor bias (i.e. where the less healthy participants are less likely to re-attend assessment). ApoB is a major component of LDL, and modifiable, where higher values are associated with poorer health generally (e.g. risk of cardiometabolic diseases)[ 24 ]. Lipids play a complex role in raising dementia risk where the magnitude and association sign vary by lipid subtype, but where ApoB generally raises AD risk[ 25 , 26 ]. Mechanistically, increased ApoB reflects the number of atherogenic lipid particles which impair the structure and function of the blood brain barrier, a proposed core aspect of dementia[ 27 ], along with myelin health[ 28 ]. ApoB also associates with subsequent stroke risk[ 29 ]. This aligns with independent observations that APOE e genotype is associated with increased risk of stroke and cerebral microbleeds, which are potentially indicative of poorer cerebrovascular health[ 30 , 31 ]. Downstream of this, ApoB levels have been shown to correlate with cerebrospinal fluid levels of Tau in asymptomatic participants (a characteristic AD neuropathology)[ 20 ]. Some previously reported associations between particular lipids and dementia may be via their inter-correlation with truly causal phenotypes[ 32 ]. Poorer cholesterol health has been identified as a modifiable contributory factor in dementia risk by the recent Lancet Commission on Dementia and our findings support this[ 33 ]. The role of APOE e genotype on risk of AD via lipids may be progressive and preventable. A clinical implication of this is the need for repeat assessments in nominally healthy people[ 34 ] at heightened genetic risk, and where lipid trajectories and APOE genotypic status may be considered together[ 35 ]. Our findings showed that while medication use (including lipid-lowering drugs) did not interact with APOE genotype, medication use did significantly improve the trajectory of ApoB levels. We observed association between APOE e4 genotype and lower – i.e. healthier – CRP trajectories. This was in an unexpected direction, i.e. where the ‘risk’ genotype for AD was associated with a marker for lower short-term inflammation. This has been reported previously in independent cohorts, with the suggestion that lower short-term inflammation leading to long-term low-grade inflammation, may confer greater lifetime risk to brain health[ 36 ]. Alternatively, healthier CRP be a correlate of prodromal weight loss preceding later dementia [ 37 , 38 ] Limitations and future research There is a fundamental need to narrow the gaps between levels of explanation that exist between exposures (including genetic), mediating biology[ 39 ], and accelerated cognitive ageing/dementia. One difficulty in this is non-coupled change, e.g. here where change in ApoB may contribute to cognitive decline but not necessarily correlate in real- or short-term. There is clear evidence that APOE e4 genotype is associated primarily with longitudinal cognitive decline rather than baseline levels[ 4 ], and understanding the mediators of that will require large-scale, multi-wave high-quality cognitive assessment plus biomarker data across the lifespan[ 40 ], where UKB currently has relatively brief repeat data (~ 4 years), in tests with mild limitations e.g. floor effects, low reliabilities[ 41 ]. There are additionally well-recognized limitations to UKB, including significant participation bias in the first instance where the n ~ 502k reflects around 5.5% of those invited[ 10 ]. There is subsequent selection bias in who returns for re-assessment[ 42 ] generally, which may influence exposure/outcome estimates of association. Summary The APOE genetic locus is a major AD risk factor where evidence suggests its influence over neurocognitive health increases with age. Its influence over mediating pathways may therefore also be progressive. Previous research had demonstrated cross-sectional associations between APOE e risk genotypes and multiple circulating blood biomarkers e.g. LDL and ApoB. The direction of these associations corresponded to AD risk; namely that e4 ‘risk’ carriers had worse trajectories and ‘protective’ e2 carriers had better (versus e3e3). Here we extend that to show APOE genotype moderates longitudinal change in lipid-related phenotypes, particularly ApoB. These variables are established dementia risk factors, and our longitudinal findings reinforce lipids as potentially targetable biological underpinnings for later-life brain health. Declarations Acknowledgements We are grateful to UK Biobank participants and staff. Thank you to Linsey Ip (University of Glasgow). for administrative support. Contributions Study concept and design: DML. Drafted original manuscript: OO AM, DML. Data analysis: OO, AM, DML. Reviewed manuscript for intellectual content: all co-authors. Funding UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council (MRC), Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. LG is funded by the King's College London DRIVE-Health Centre for Doctoral Training and the Perron Institute for Neurological and Translational Science. RJS was supported by a University of Glasgow LKAS fellowship and a UKRI Innovation-HDR-UK Fellowship (MR/S003061/1). L.M.L. is funded by The John, Margaret, Alfred and Stewart Sim Fellowship, and a University of Glasgow Lord Kelvin Adam Smith Fellowship Competing interests The authors have nothing to disclose. References Uddin MS, Kabir MT, Al Mamun A, Abdel-Daim MM, Barreto GE, Ashraf GM. APOE and Alzheimer’s Disease: Evidence Mounts that Targeting APOE4 may Combat Alzheimer’s Pathogenesis. Mol Neurobiol. 2019. Williams DM, Davies NM, Anderson Phd EL. The proportion of Alzheimer’s disease attributable to apolipoprotein E. MedRxiv. 2024:2023.11.16.23298475. Bu G. Apolipoprotein E and its receptors in Alzheimer’s disease: pathways, pathogenesis and therapy. Nature Reviews Neuroscience 2009 10:5. 2009;10:333–344. Deary IJ, Whiteman MC, Pattie A, Starr JM, Hayward C, Wright AF, et al. Cognitive change and the APOE ɛ4 allele. Nature 2002 418:6901. 2002;418:932–932. 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Tables Table 1: linear regression estimates of change in blood biomarkers, by apolipoprotein e4 presence (versus e3e3) Variable Partially-adjusted model Fully-adjusted model Estimate Lower 95% CI Upper 95% CI P-value N Estimate Lower 95% CI Upper 95% CI P-value N IGF-1 -0.072 -0.208 0.063 0.296 12182 -0.074 -0.210 0.062 0.287 12094 CRP -0.505 -0.652 -0.357 <0.001 12325 -0.508 -0.655 -0.361 <0.001 12236 Vitamin D -0.409 -1.127 0.310 0.265 11419 -0.445 -1.166 0.276 0.226 11339 Triglycerides 0.027 0.000 0.055 0.053 12352 0.021 -0.006 0.049 0.132 12263 Protein 0.022 -0.130 0.173 0.777 9988 0.026 -0.126 0.178 0.735 9917 Testosterone 0.013 -0.078 0.104 0.775 10574 0.024 -0.067 0.115 0.609 10496 Bilirubin -0.010 -0.116 0.097 0.861 12255 -0.003 -0.110 0.105 0.959 12166 SHGB 0.522 -0.130 1.174 0.117 9767 0.663 0.011 1.314 0.046 9700 Phosphate 0.002 -0.003 0.008 0.435 9965 0.003 -0.003 0.008 0.396 9894 Lipoprotein A -0.085 -0.737 0.567 0.798 9142 -0.094 -0.748 0.560 0.778 9070 HbA1C -0.040 -0.197 0.117 0.619 9562 -0.062 -0.219 0.094 0.435 9496 Random Glucose -0.020 -0.057 0.017 0.298 9980 -0.024 -0.061 0.013 0.200 9909 GGT -1.484 -2.497 -0.471 0.004 12366 -1.524 -2.543 -0.506 0.003 12277 Cystatin-C 0.000 -0.004 0.003 0.870 12362 0.000 -0.004 0.003 0.812 12273 ApoB 0.016 0.010 0.023 <0.001 12297 0.018 0.012 0.025 <0.001 12209 ApoA -0.003 -0.010 0.004 0.399 9901 -0.002 -0.010 0.005 0.532 9830 Creatinine -0.020 -0.403 0.363 0.918 12350 -0.025 -0.410 0.359 0.898 12261 Total cholesterol 0.046 0.012 0.079 0.008 12377 0.055 0.022 0.088 0.001 12288 Calcium -0.001 -0.004 0.003 0.767 9997 -0.001 -0.004 0.003 0.740 9926 Bilirubin -0.002 -0.027 0.022 0.847 9430 -0.001 -0.025 0.024 0.951 9363 Aspartate 0.011 -0.290 0.312 0.945 12282 0.011 -0.292 0.313 0.944 12193 HDL -0.002 -0.011 0.007 0.645 9992 -0.001 -0.010 0.008 0.820 9921 LDL 0.040 0.015 0.066 0.002 12317 0.048 0.022 0.074 <0.001 12228 Alanine -0.424 -0.824 -0.024 0.038 12372 -0.459 -0.860 -0.058 0.025 12283 Alkaline phosphate 0.087 -0.546 0.720 0.789 12372 0.043 -0.593 0.679 0.894 12283 Albumin 0.059 -0.037 0.155 0.231 9997 0.055 -0.042 0.151 0.267 9926 95% CI = 95% confidence intervals. Significant P<0.002 highlighted in bold. CRP = C reactive Protein. IGF-1 = insulin-like growth factor 1. SHGB = Sex hormone binding globulin. Hba1C = glycated haemoglobin. H/LDL = high/low density lipoproteins. Partially adjusted: controlling for age at baseline, sex, 10 principal components for stratification, timepoint interval, and respective baseline biomarker values. Fully adjusted: additionally controlling for Townsend, medication at baseline and ever-smoking status. Table 2: linear regression estimates of change in blood biomarkers, by apolipoprotein e2 presence (versus e3e3) Variable Partially-adjusted model Fully-adjusted model Estimate Lower 95% CI Upper 95% CI P-value N Estimate Lower 95% CI Upper 95% CI P-value N IGF-1 -0.060 -0.238 0.118 0.507 10224 -0.041 -0.220 0.138 0.653 10137 CRP 0.078 -0.132 0.289 0.466 10344 0.102 -0.108 0.312 0.340 10256 Vitamin D -0.618 -1.539 0.303 0.188 9735 -0.684 -1.610 0.243 0.148 9653 Triglycerides 0.049 0.013 0.085 0.008 10360 0.051 0.015 0.088 0.006 10273 Protein -0.091 -0.290 0.107 0.368 8391 -0.082 -0.282 0.118 0.422 8320 Testosterone 0.002 -0.116 0.120 0.972 8853 -0.003 -0.121 0.115 0.961 8780 Bilirubin -0.010 -0.149 0.128 0.882 10280 0.000 -0.140 0.139 0.998 10193 SHGB -0.073 -0.928 0.783 0.867 8225 -0.124 -0.978 0.730 0.776 8160 Phosphate -0.002 -0.010 0.006 0.617 8370 -0.002 -0.010 0.006 0.606 8299 Lipoprotein A -0.878 -1.759 0.004 0.051 7674 -0.846 -1.730 0.038 0.061 7610 HbA1C -0.070 -0.276 0.135 0.504 8065 -0.032 -0.238 0.174 0.761 8000 Random Glucose 0.014 -0.035 0.063 0.578 8387 0.022 -0.026 0.071 0.368 8316 GGT -0.199 -1.605 1.208 0.782 10378 -0.171 -1.589 1.246 0.813 10290 Cystatin-C -0.002 -0.006 0.003 0.505 10378 -0.001 -0.005 0.004 0.788 10290 ApoB -0.050 -0.059 -0.041 <0.001 10307 -0.054 -0.063 -0.045 <0.001 10219 ApoA 0.007 -0.003 0.016 0.190 8306 0.006 -0.003 0.016 0.204 8235 Creatinine 0.071 -0.414 0.555 0.775 10364 0.100 -0.387 0.586 0.689 10277 Total cholesterol -0.155 -0.198 -0.112 <0.001 10388 -0.172 -0.215 -0.129 <0.001 10300 Calcium -0.005 -0.009 0.000 0.069 8398 -0.004 -0.009 0.001 0.122 8327 Bilirubin 0.026 -0.007 0.059 0.119 7957 0.031 -0.002 0.064 0.067 7887 Aspartate 0.536 0.009 1.062 0.046 10311 0.538 0.007 1.069 0.047 10223 HDL 0.005 -0.006 0.017 0.376 8396 0.005 -0.007 0.017 0.416 8325 LDL -0.153 -0.186 -0.119 <0.001 10336 -0.168 -0.201 -0.135 <0.001 10248 Alanine 0.136 -0.405 0.677 0.622 10384 0.185 -0.359 0.730 0.504 10296 Alkaline phosphate -0.085 -0.904 0.734 0.839 10385 -0.054 -0.879 0.770 0.897 10297 Albumin -0.055 -0.181 0.070 0.389 8400 -0.038 -0.165 0.088 0.554 8330 95% CI = 95% confidence intervals. Significant P<0.002 highlighted in bold. CRP = C reactive Protein. IGF-1 = insulin-like growth factor 1. SHGB = Sex hormone binding globulin. Hba1C = glycated haemoglobin. H/LDL = high/low density lipoproteins. Partially adjusted: controlling for age at baseline, sex, 10 principal components for stratification, timepoint interval, and respective baseline biomarker values. Fully adjusted: additionally controlling for Townsend, medication at baseline and ever-smoking status. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Lyallsuppl02june25.xlsx Supplementary Material Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6802545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496100491,"identity":"5997ad0b-9ee5-449b-a2dd-ec89faea498d","order_by":0,"name":"Donald Lyall","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4565-9656","institution":"University of Glasgow","correspondingAuthor":true,"prefix":"","firstName":"Donald","middleName":"","lastName":"Lyall","suffix":""},{"id":496100492,"identity":"a4c44c48-6ee3-443c-b38a-b58799c19eaf","order_by":1,"name":"Oluwatobi Oni","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Oluwatobi","middleName":"","lastName":"Oni","suffix":""},{"id":496100493,"identity":"9f11d0ac-d9a4-437e-9932-6ba2540cb2c9","order_by":2,"name":"Amy McKie","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"McKie","suffix":""},{"id":496100494,"identity":"d1c59aef-f03b-470a-8fc1-da0487fc7485","order_by":3,"name":"Jack Beazer","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"","lastName":"Beazer","suffix":""},{"id":496100495,"identity":"eb711546-2fee-405d-8517-d33373e7d2d3","order_by":4,"name":"Angelina Kancheva","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Angelina","middleName":"","lastName":"Kancheva","suffix":""},{"id":496100496,"identity":"2cd5e901-a3b5-491d-ba16-546204cd07f3","order_by":5,"name":"Rachana Tank","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rachana","middleName":"","lastName":"Tank","suffix":""},{"id":496100497,"identity":"6eaee135-2cfd-423a-99c5-476b88f5d33f","order_by":6,"name":"Josie Fullerton","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Josie","middleName":"","lastName":"Fullerton","suffix":""},{"id":496100498,"identity":"bf1b7cf7-a750-4283-bc25-84806b8a153b","order_by":7,"name":"Lachlan Gilchrist","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lachlan","middleName":"","lastName":"Gilchrist","suffix":""},{"id":496100499,"identity":"4b647cef-b951-45d6-9faa-47cc11e0623c","order_by":8,"name":"Laura Lyall","email":"","orcid":"","institution":"[email protected]","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Lyall","suffix":""},{"id":496100500,"identity":"724774a3-57f0-4b08-98fc-26552daacc66","order_by":9,"name":"Saraid McIlvride","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Saraid","middleName":"","lastName":"McIlvride","suffix":""},{"id":496100501,"identity":"70ceed52-930f-419e-8a9d-7128d3fccd2a","order_by":10,"name":"Amy Ferguson","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"","lastName":"Ferguson","suffix":""},{"id":496100502,"identity":"5bf72b84-ff55-4744-beac-565a00304919","order_by":11,"name":"Rona Strawbridge","email":"","orcid":"https://orcid.org/0000-0001-8506-3585","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Rona","middleName":"","lastName":"Strawbridge","suffix":""},{"id":496100503,"identity":"b1f3d611-89a0-42db-b579-9c9e8544b698","order_by":12,"name":"Simon Cox","email":"","orcid":"https://orcid.org/0000-0003-4036-3642","institution":"Lothian Birth Cohorts, Dept. Psychology","correspondingAuthor":false,"prefix":"","firstName":"Simon","middleName":"","lastName":"Cox","suffix":""},{"id":496100504,"identity":"87f7d2e4-3e9a-4e79-80db-0575361fa686","order_by":13,"name":"Ian Deary","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Deary","suffix":""},{"id":496100505,"identity":"7c29da15-c26c-46eb-b68f-e2e8b4511ede","order_by":14,"name":"William Stewart","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Stewart","suffix":""},{"id":496100506,"identity":"4a13026b-e39e-4841-8d98-0bee3afaa0d5","order_by":15,"name":"Ann-Marie de Lange","email":"","orcid":"https://orcid.org/0000-0002-5150-6656","institution":"University of Lausanne","correspondingAuthor":false,"prefix":"","firstName":"Ann-Marie","middleName":"","lastName":"de Lange","suffix":""},{"id":496100507,"identity":"49f5d5bc-b39e-4a87-accf-dacdb8c537d3","order_by":16,"name":"Paul Welsh","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Welsh","suffix":""},{"id":496100508,"identity":"5b4f4567-d77a-4720-b5c5-347a63a03d04","order_by":17,"name":"Naveed Sattar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Naveed","middleName":"","lastName":"Sattar","suffix":""}],"badges":[],"createdAt":"2025-06-02 13:20:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6802545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6802545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88950715,"identity":"7f210089-8d14-47bd-ae52-55f1b4cdab64","added_by":"auto","created_at":"2025-08-13 05:49:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eestimated marginal mean change values for apolipoprotein b (ApoB) across two timepoints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: adjusted for age at baseline, sex, ever-smoking status, deprivation, timepoint interval, 10 principal components for ancestral stratification, genotypic chip and baseline ApoB levels. Values are: e2e2 (-0.139 change between timepoints 1 and 2, standard error [SE] = 0.021); e2e3 (-0.051, 0.004); e2e4 (-0.020, SE = 0.009), e3e3 (0.001, SE = 0.002), e3e4 (0.020, SE = 0.003), e4e4 (0.011, SE = 0.009). Bars reflect 95% confidence intervals.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6802545/v1/5c93287c9f89ffdd91c8eb9b.png"},{"id":89859016,"identity":"abff148e-c8f0-4fbf-bee5-e9257eccd48f","added_by":"auto","created_at":"2025-08-25 20:13:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1421577,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6802545/v1/f278d643-1c0b-4c79-9df1-431fc2e7cac4.pdf"},{"id":88950720,"identity":"235b423f-65c9-492f-9f50-338736e270ac","added_by":"auto","created_at":"2025-08-13 05:49:49","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":668808,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"Lyallsuppl02june25.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6802545/v1/d04caee0d3273f5a40345193.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Genetic risk for Alzheimer’s disease, and differential trajectories in circulating blood biomarkers in UK Biobank (n=17,817)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the pathways from human genetic differences towards ultimate disease manifestation is a scientific and clinical priority because this may lead to targetable, preventive approaches. Apolipoprotein \u003cem\u003e(APOE)\u003c/em\u003e e genotype is the largest risk factor for Alzheimer\u0026rsquo;s disease (AD) dementia after increasing age, and the e4 allele is associated with earlier age at disease onset and accelerated cognitive ageing generally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Around 42% of AD can be statistically attributed to \u003cem\u003eAPOE\u003c/em\u003e e genotype[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] (via population attributable fraction). \u003cem\u003eAPOE\u003c/em\u003e is highly pleiotropic, influencing multiple biological functions partly via a dominant role in lipid metabolism[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData from cohort studies demonstrates that \u003cem\u003eAPOE\u003c/em\u003e genotype\u0026rsquo;s influence on brain health increases over time - having little to no influence in early/mid-life, but significantly so in later life[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Broadly, it seems to influence change in relative brain health, rather than baseline levels[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This \u0026lsquo;change influence\u0026rsquo; may manifest in other health phenotypes across the lifespan. Investigating the potential influence of \u003cem\u003eAPOE\u003c/em\u003e genotype, a major dementia genetic risk factor, on change across timepoints in circulating blood biomarkers which are indicative of multiple aspects of health, could provide a greater understanding of the seemingly time-dependent mechanisms linking \u003cem\u003eAPOE\u003c/em\u003e to dementia and age-related cognitive decline.\u003c/p\u003e\u003cp\u003eWe have previously demonstrated widespread associations between \u003cem\u003eAPOE\u003c/em\u003e genotype and cross-sectional blood biomarker levels[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In that study of N\u0026thinsp;~\u0026thinsp;396k UK Biobank (UKB) participants, the largest associations were found with total cholesterol (0.13 standard deviations/SDs difference per e4 allele), ApoB (0.20 SDs), and LDL (0.15 SDs), with e2 genotype associations generally in the correspondingly opposite, protective direction. This is important because it shows influence of AD-related genetic influence on known potential dementia risk factors like LDL[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], possibly years before disease onset. In genetic-biomarker studies, cross-sectional associations may reflect a mixture of variance in baseline levels of the biomarker and the degree of change in it across time. As such, longitudinal data are often considered additionally informative and a more stringent test for potential causal hypotheses. Here, in ~\u0026thinsp;18,000 participants with two-wave longitudinal biomarker data from UKB, we tested associations between \u003cem\u003eAPOE\u003c/em\u003e e2 \u0026lsquo;protective\u0026rsquo; and e4 \u0026lsquo;risk\u0026rsquo; genotypes, and ~\u0026thinsp;4-year change in 26 common clinical measurements associated with health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eUKB is a prospective general population cohort, where approximately 502,000 participants attended one of 22 assessment centres in Scotland, England and Wales, between 2006 and 2010[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Participants were aged 40\u0026ndash;70 years at baseline. Approximately 4 years later, a sub-sample of n\u0026thinsp;=\u0026thinsp;20,339 underwent repeat assessment, in one centre (Stockport). Biomarkers were assessed at both visits. This research was completed using UKB project #17689.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthics\u003c/h3\u003e\n\u003cp\u003eParticipants provided full informed consent to participate in UK Biobank. This study was covered by the generic ethical approval for UK Biobank studies from the NHS National Research Ethics Service (approval letter dated 17th June 2011, Ref 11/NW/0382).\u003c/p\u003e\n\u003ch3\u003eGenotyping\u003c/h3\u003e\n\u003cp\u003eUK Biobank participants were genotyped using Applied Biosystems UK BiLEVE Axiom array by Affymetrix and Applied Biosystems UK Biobank Axiom Array which share 95% marker content [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. \u003cem\u003eAPOE\u003c/em\u003e e status was based on two single nucleotide polymorphisms (SNPs): rs7412 and rs429358. Stringent quality control and processing were applied to the data, detailed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/scientists-3/genetic-data\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/scientists-3/genetic-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf\u003c/span\u003e\u003cspan address=\"http://www.ukbiobank.ac.uk/wp-content/uploads/2014/04/UKBiobank_genotyping_QC_documentation-web.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Genetic principal components were calculated by UK Biobank. The polygenic risk score for AD was calculated using an infinitesimal model with LDPred software[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. A total of 6,578,321 SNPs (including imputed single nucleotide polymorphisms [SNPs] at a minimum 80% confidence) with varying effect estimates associated with late-onset AD, from a previous genome-wide association study, were included to calculate weighted risk scores[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBiomarker processing\u003c/h3\u003e\n\u003cp\u003eWe examined 26 circulating blood biomarker phenotypes which are in common clinical use and were selected on the basis of being established risk factors for certain conditions, diagnostic tools, or to examine health in otherwise difficult-to-assess phenotypes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with additional information on the UKB website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ukbiobank.ac.uk/media/oiudpjqa/bcm023_ukb_biomarker_panel_website_v1-0-aug-2015-edit-2018.pdf\u003c/span\u003e\u003cspan address=\"https://www.ukbiobank.ac.uk/media/oiudpjqa/bcm023_ukb_biomarker_panel_website_v1-0-aug-2015-edit-2018.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Biomarker levels were analysed in UKB from serum and packed red blood cell samples obtained from all UKB participants at both visits [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Of all the measures on the panel, we did not assess change in oestrodiol or rheumatoid factor because large numbers (\u0026gt;\u0026thinsp;80%) of participants had missing data, likely due to \u0026lsquo;true\u0026rsquo; values below the lowest measurable level[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Stringent quality controls were applied to the assays used measure biomarker levels. Details of biomarker quality control, instrumentation and analysis methods are available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/showcase/showcase/docs/biomarker_issues.pdf\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/biomarker_issues.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf\u003c/span\u003e\u003cspan address=\"https://biobank.ndph.ox.ac.uk/showcase/showcase/docs/serum_biochemistry.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biobank.ndph.ox.ac.uk/showcase/showcase/docs/haematology.pdf\u003c/span\u003e\u003cspan address=\"http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/haematology.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eAge was calculated by UKB based on date of birth versus date of assessment. Sex and smoking history were self-reported, the latter as current, past or never. We collated past and current smokers into \u0026lsquo;ever\u0026rsquo; (vs. never). Participants separately self-reported current medication for lowering lipid, insulin, or blood pressure levels and this was binarized (yes; no). Townsend scores indicate area-based deprivation based on participant postcode, where higher scores reflect greater deprivation.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQuality controlling\u003c/h2\u003e\u003cp\u003eWe excluded participants with non-white British ancestry due to observed \u003cem\u003eAPOE\u003c/em\u003e genotypic frequency differences between ethnicities[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], sex mismatch (self-report versus genetic), chromosomal aneuploidy, excessive heterozygosity and genotype missing rate\u0026thinsp;\u0026gt;\u0026thinsp;10%. In instances of 1st degree relatedness we removed one participant at random. We did not include the minority of participants with e2/e4 (\u0026lt;\u0026thinsp;1%) genotype in effect contrasts because this included both potentially protective and risk alleles. We removed participants with \u0026lt;\u0026thinsp;100 days assessment interval (n\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePrimary analyses\u003c/h3\u003e\n\u003cp\u003eWe firstly tested for absolute differences in biomarker values across waves, using Students and Mann-Whitney (non-parametric equivalent) T-tests. For uncorrected change statistics across the two timepoints, Cohen\u0026rsquo;s D standardized effect sizes are provided (where 0.2 reflects a small effect size and 0.5 is considered medium), and matched rank biserial correlation statistics for non-parametric equivalents. We tested descriptive inter-correlations between all timepoint 1 biomarkers with unadjusted Pearson correlations. We subsequently used linear regression to test for associations between possession of the \u003cem\u003eAPOE\u003c/em\u003e e4 and e2 alleles versus e3e3 neutral genotype, and change in biomarkers. The key outcome was timepoint 2 biomarker values adjusted for timepoint 1 values (i.e. residualized change)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We adjusted for covariates of respective baseline biomarker values, age at baseline, sex, Townsend deprivation index, ever-smoking history, timepoint interval, medication yes/no, genotypic chip, and 10 genetic principal components (PCs) for ancestral stratification.\u003c/p\u003e\u003cp\u003eAnalyses were run in two models: partially-adjusted (age; sex; baseline biomarker value; timepoint interval in days; chip and 10 PCs) and fully-adjusted (all covariates). We conservatively considered P\u0026thinsp;\u0026le;\u0026thinsp;0.001 significant, and report unstandardized parameter estimates. For significant findings we also report standardized betas (i.e. on the per-SD scale) for ease of interpretation, in the text.\u003c/p\u003e\n\u003ch3\u003eSecondary analyses\u003c/h3\u003e\n\u003cp\u003eWe subsequently tested for associations of non-\u003cem\u003eAPOE\u003c/em\u003e polygenic risk for AD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] with biomarker change. This was based on a non-UKB genome-wide association study (GWAS). Polygenic risk scores were standardised to Z scores, i.e. mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1 such that higher scores reflect increased risk. These analyses controlled for individual \u003cem\u003eAPOE\u003c/em\u003e e genotypic status, as a factor. We tested for interactions between \u003cem\u003eAPOE\u003c/em\u003e genotypes with male/female sex, and separately baseline age, on outcomes. Statistical packages were PLINK 1.90 (for APOE and PGR derivation), Stata 18 for analyses and JASP 0.19.1 for visualization.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, N\u0026thinsp;=\u0026thinsp;17,817 participants had \u003cem\u003eAPOE\u003c/em\u003e e genotypic and two-wave biomarker data after exclusions. The average age at baseline was 57.39 (SD\u0026thinsp;=\u0026thinsp;7.33), and 9,081 (51%) participants were female. The average interval between baseline and repeat assessments was 4.30 years (SD\u0026thinsp;=\u0026thinsp;0.92, range\u0026thinsp;=\u0026thinsp;2.11 to 7.0). Paired-sample T-tests showed significant uncorrected changes in all biomarkers between timepoint 1 and timepoint 2 (Supplementary Table\u0026nbsp;1), with standardized effect sizes typically around 0.1 (i.e. \u0026lsquo;small\u0026rsquo; effect) based on Cohen\u0026rsquo;s D standardized metrics. Supplementary Table\u0026nbsp;2 shows inter-correlations between the different biomarkers, of which the majority were significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.001. \u003cem\u003eAPOE\u003c/em\u003e e genotypic frequencies are shown in Supplementary Table\u0026nbsp;3.\u003c/p\u003e\u003cp\u003e\u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAPOE genotype and change in biomarker values\u003c/h2\u003e\u003cp\u003ePartially and fully-adjusted models generally had very similar results. For e4 allele presence (versus e3e3), four change associations (out of 26) were significant at P\u0026thinsp;\u0026le;\u0026thinsp;0.001, and these are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These were: CRP (standardized β = -0.059, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 relative to e3e3), ApoB (standardized β\u0026thinsp;=\u0026thinsp;0.036, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total cholesterol (standardized β\u0026thinsp;=\u0026thinsp;0.021, P\u0026thinsp;=\u0026thinsp;0.001) and LDL (standardized β\u0026thinsp;=\u0026thinsp;0.025, P\u0026thinsp;=\u0026thinsp;0.001). Possession of \u003cem\u003eAPOE\u003c/em\u003e e4 was associated with change in a protective direction for CRP, and deleteriously (i.e. change in an unhealthy direction) for ApoB, total cholesterol, and LDL. This means that that carriers of an \u003cem\u003eAPOE\u003c/em\u003e e4 allele showed above-average change in lipids, in the direction of poorer health, relative to neutral e3/e3 genotype group.\u003c/p\u003e\u003cp\u003e\u003cb\u003e[Insert\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor e2 presence (versus neutral e3e3), there were three significant associations out of 26. These were change in ApoB (standardized β = -0.090, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 relative to e3e3), total cholesterol (standardized β = -0.057, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and LDL (standardized β = -0.074, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). \u003cem\u003eAPOE\u003c/em\u003e e2 was associated in protective directions for ApoB, total cholesterol and LDL (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe strongest, most consistent \u003cem\u003eAPOE\u003c/em\u003e genotypic association was found for change in ApoB. This followed an e2/e3/e4 dose-response risk gradient of: e2e2; e2e3; e2e4; e3e3; e3e4; e4e4 (parameter estimate\u0026thinsp;=\u0026thinsp;0.022, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 average increase per allelic difference). Estimated marginal mean change values are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and trajectories for ApoB between timepoints 1 and 2 stratified by \u003cem\u003eAPOE\u003c/em\u003e genotype, are shown in Supplementary Fig.\u0026nbsp;1. Full model results for ApoB change are shown in Supplementary Table\u0026nbsp;4, where the largest non-\u003cem\u003eAPOE\u003c/em\u003e genotypic associations are male versus female sex (parameter estimate = -0.044, 95% CI = -0.050 to -0.037, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and medication use (parameter estimate = -0.037, 95% CI = -0.044 to -0.029, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This means that on average the ApoB levels of males, and participants on medication, typically got lower across timepoints (female estimated marginal mean change\u0026thinsp;=\u0026thinsp;0.020 g/L versus male \u0026minus;\u0026thinsp;0.024 g/L; no-medication average change\u0026thinsp;=\u0026thinsp;0.009 g/L; on-medication average change = -0.028).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e[Insert\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003ehere]\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eNon-APOE polygenic risk for Alzheimer\u0026rsquo;s disease, and biomarker change values\u003c/h2\u003e\u003cp\u003eThere were no significant associations between polygenic risk for AD and biomarker change values, controlling for \u003cem\u003eAPOE\u003c/em\u003e e genotype (Supplementary Table\u0026nbsp;5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInteractions between APOE genotype with sex, age, medication history, and sensitivity analyses\u003c/h2\u003e\u003cp\u003eThere was no evidence of interaction between \u003cem\u003eAPOE\u003c/em\u003e genotype and sex on biomarker changes at our conservative significance threshold (lowest P\u0026thinsp;=\u0026thinsp;0.010, for glucose/e4 presence), nor medication (lowest P\u0026thinsp;=\u0026thinsp;0.009 for e2 genotype versus e3e3, yes/no medication and aspartate). There were no significant interactions with age (lowest P-value\u0026thinsp;=\u0026thinsp;ApoB and e2, interaction p\u0026thinsp;=\u0026thinsp;0.007). Correspondingly, the findings were very similar when we re-ran associations in older participants aged 65 years and above.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e e genotype is a moderator of age-related cognitive decline including risk of dementia[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Understanding its influence on premorbid phenotypes, in people with no evidence of dementia, may inform part of the pathway from genetic variation to poorer later-life brain health[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Having previously shown widespread cross-sectional associations between \u003cem\u003eAPOE\u003c/em\u003e genotype with multiple biomarkers (e.g. LDL, ApoB, HbA1c) in N\u0026thinsp;~\u0026thinsp;396k[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], here we extend that. \u003cem\u003eAPOE\u003c/em\u003e e genotype significantly modifies longitudinal trajectories in biomarker levels across approximately 4 years. This is most prominent for lipids, e.g. ApoB. Relative to neutral e3e3 genotype, \u0026lsquo;risk\u0026rsquo; e4 carriers tended to have less healthy trajectories, and e2 \u0026lsquo;protective\u0026rsquo; carriers tended to have protective trajectories. These associations generally survived correction for potential confounders e.g. deprivation. These findings were not modified by age at time of assessment, sex, or medication history, and were evident in the cohort including people from late middle-age, i.e. not necessarily at a typical age of dementia onset.\u003c/p\u003e\u003cp\u003eOur findings add nuance to the proposed causal association between lipids and dementia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], namely that \u003cem\u003eAPOE\u003c/em\u003e genetic variation contributes to differences in lipid levels[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] increasingly across time[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. \u003cem\u003eAPOE\u003c/em\u003e genotype seems to primarily associate particularly with poorer older-age rather than early/mid-life cognitive health[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], suggesting progressive influence over the lifespan. Association with baseline but also longitudinal change in lipids may feasibly mediate some of \u003cem\u003eAPOE\u003c/em\u003e genotype age-dependent association with brain health.\u003c/p\u003e\u003cp\u003eThe largest \u003cem\u003eAPOE\u003c/em\u003e change association was found for ApoB, and this broadly followed a \u0026lsquo;risk\u0026rsquo; gradient associated with \u003cem\u003eAPOE\u003c/em\u003e genotype and AD: namely e2/e2 carriers showed the healthiest trajectory, e3 neutral and e4 carriers generally worse across time. E4 homozygotes did not perfectly continue this linear trajectory as would be expected, and this may reflect some aspect of power or attrition/survivor bias (i.e. where the less healthy participants are less likely to re-attend assessment). ApoB is a major component of LDL, and modifiable, where higher values are associated with poorer health generally (e.g. risk of cardiometabolic diseases)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Lipids play a complex role in raising dementia risk where the magnitude and association sign vary by lipid subtype, but where ApoB generally raises AD risk[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Mechanistically, increased ApoB reflects the number of atherogenic lipid particles which impair the structure and function of the blood brain barrier, a proposed core aspect of dementia[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], along with myelin health[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. ApoB also associates with subsequent stroke risk[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This aligns with independent observations that \u003cem\u003eAPOE\u003c/em\u003e e genotype is associated with increased risk of stroke and cerebral microbleeds, which are potentially indicative of poorer cerebrovascular health[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Downstream of this, ApoB levels have been shown to correlate with cerebrospinal fluid levels of Tau in asymptomatic participants (a characteristic AD neuropathology)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Some previously reported associations between particular lipids and dementia may be via their inter-correlation with truly causal phenotypes[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePoorer cholesterol health has been identified as a modifiable contributory factor in dementia risk by the recent Lancet Commission on Dementia and our findings support this[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The role of \u003cem\u003eAPOE\u003c/em\u003e e genotype on risk of AD via lipids may be progressive and preventable. A clinical implication of this is the need for repeat assessments in nominally healthy people[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] at heightened genetic risk, and where lipid trajectories and \u003cem\u003eAPOE\u003c/em\u003e genotypic status may be considered together[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Our findings showed that while medication use (including lipid-lowering drugs) did not interact with \u003cem\u003eAPOE\u003c/em\u003e genotype, medication use did significantly improve the trajectory of ApoB levels.\u003c/p\u003e\u003cp\u003eWe observed association between \u003cem\u003eAPOE\u003c/em\u003e e4 genotype and lower \u0026ndash; i.e. healthier \u0026ndash; CRP trajectories. This was in an unexpected direction, i.e. where the \u0026lsquo;risk\u0026rsquo; genotype for AD was associated with a marker for lower short-term inflammation. This has been reported previously in independent cohorts, with the suggestion that lower short-term inflammation leading to long-term low-grade inflammation, may confer greater lifetime risk to brain health[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Alternatively, healthier CRP be a correlate of prodromal weight loss preceding later dementia [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future research\u003c/h2\u003e\u003cp\u003eThere is a fundamental need to narrow the gaps between levels of explanation that exist between exposures (including genetic), mediating biology[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and accelerated cognitive ageing/dementia. One difficulty in this is non-coupled change, e.g. here where change in ApoB may contribute to cognitive decline but not necessarily correlate in real- or short-term. There is clear evidence that \u003cem\u003eAPOE\u003c/em\u003e e4 genotype is associated primarily with longitudinal cognitive decline rather than baseline levels[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and understanding the mediators of that will require large-scale, multi-wave high-quality cognitive assessment plus biomarker data across the lifespan[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], where UKB currently has relatively brief repeat data (~\u0026thinsp;4 years), in tests with mild limitations e.g. floor effects, low reliabilities[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. There are additionally well-recognized limitations to UKB, including significant participation bias in the first instance where the n\u0026thinsp;~\u0026thinsp;502k reflects around 5.5% of those invited[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There is subsequent selection bias in who returns for re-assessment[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] generally, which may influence exposure/outcome estimates of association.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSummary\u003c/h2\u003e\u003cp\u003eThe \u003cem\u003eAPOE\u003c/em\u003e genetic locus is a major AD risk factor where evidence suggests its influence over neurocognitive health increases with age. Its influence over mediating pathways may therefore also be progressive. Previous research had demonstrated cross-sectional associations between \u003cem\u003eAPOE\u003c/em\u003e e risk genotypes and multiple circulating blood biomarkers e.g. LDL and ApoB. The direction of these associations corresponded to AD risk; namely that e4 \u0026lsquo;risk\u0026rsquo; carriers had worse trajectories and \u0026lsquo;protective\u0026rsquo; e2 carriers had better (versus e3e3). Here we extend that to show \u003cem\u003eAPOE\u003c/em\u003e genotype moderates longitudinal change in lipid-related phenotypes, particularly ApoB. These variables are established dementia risk factors, and our longitudinal findings reinforce lipids as potentially targetable biological underpinnings for later-life brain health.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to UK Biobank participants and staff. Thank you to Linsey Ip (University of Glasgow). for administrative support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: DML.\u003c/p\u003e\n\u003cp\u003eDrafted original manuscript: OO AM, DML.\u003c/p\u003e\n\u003cp\u003eData analysis: OO, AM, DML.\u003c/p\u003e\n\u003cp\u003eReviewed manuscript for intellectual content: all co-authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK Biobank was established by the Wellcome Trust medical charity, Medical Research Council (MRC), Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. LG is funded by the King's College London DRIVE-Health Centre for Doctoral Training and the Perron Institute for Neurological and Translational Science. RJS was supported by a University of Glasgow LKAS fellowship and a UKRI Innovation-HDR-UK Fellowship (MR/S003061/1). L.M.L. is funded by The John, Margaret, Alfred and Stewart Sim Fellowship, and a University of Glasgow Lord Kelvin Adam Smith Fellowship\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUddin MS, Kabir MT, Al Mamun A, Abdel-Daim MM, Barreto GE, Ashraf GM. APOE and Alzheimer\u0026rsquo;s Disease: Evidence Mounts that Targeting APOE4 may Combat Alzheimer\u0026rsquo;s Pathogenesis. Mol Neurobiol. 2019.\u003c/li\u003e\n\u003cli\u003eWilliams DM, Davies NM, Anderson Phd EL. The proportion of Alzheimer\u0026rsquo;s disease attributable to apolipoprotein E. MedRxiv. 2024:2023.11.16.23298475.\u003c/li\u003e\n\u003cli\u003eBu G. Apolipoprotein E and its receptors in Alzheimer\u0026rsquo;s disease: pathways, pathogenesis and therapy. Nature Reviews Neuroscience 2009 10:5. 2009;10:333\u0026ndash;344.\u003c/li\u003e\n\u003cli\u003eDeary IJ, Whiteman MC, Pattie A, Starr JM, Hayward C, Wright AF, et al. Cognitive change and the APOE ɛ4 allele. Nature 2002 418:6901. 2002;418:932\u0026ndash;932.\u003c/li\u003e\n\u003cli\u003eRitchie K, Carri\u0026egrave;re I, Su L, O\u0026rsquo;Brien JT, Lovestone S, Wells K, et al. The midlife cognitive profiles of adults at high risk of late-onset Alzheimer\u0026rsquo;s disease: The PREVENT study. Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2017;13:1089\u0026ndash;1097.\u003c/li\u003e\n\u003cli\u003eDavies G, Armstrong N, Bis JC, Bressler J, Chouraki V, Giddaluru S, et al. Genetic contributions to variation in general cognitive function: a meta-analysis of genome-wide association studies in the CHARGE consortium (N=53\u0026thinsp;949). Molecular Psychiatry 2015 20:2. 2015;20:183\u0026ndash;192.\u003c/li\u003e\n\u003cli\u003eLyall DM, Harris SE, Bastin ME, Mu\u0026ntilde;oz Maniega S, Murray C, Lutz MW, et al. Are APOE ɛ genotype and TOMM40 poly-T repeat length associations with cognitive ageing mediated by brain white matter tract integrity? Transl Psychiatry. 2014;4:e449.\u003c/li\u003e\n\u003cli\u003eFerguson AC, Tank R, Lyall LM, Ward J, Celis-Morales C, Strawbridge R, et al. Alzheimer\u0026rsquo;s Disease Susceptibility Gene Apolipoprotein E (APOE) and Blood Biomarkers in UK Biobank (N = 395,769). Journal of Alzheimer\u0026rsquo;s Disease. 2020;76:1541\u0026ndash;1551.\u003c/li\u003e\n\u003cli\u003eOlmastroni E, Molari G, De Beni N, Colpani O, Galimberti F, Gazzotti M, et al. Statin use and risk of dementia or Alzheimer\u0026rsquo;s disease: a systematic review and meta-analysis of observational studies. Eur J Prev Cardiol. 2022;29:804\u0026ndash;814.\u003c/li\u003e\n\u003cli\u003eSudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015;12:e1001779.\u003c/li\u003e\n\u003cli\u003eBycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018. 2018. https://doi.org/10.1038/s41586-018-0579-z.\u003c/li\u003e\n\u003cli\u003eVilhj\u0026aacute;lmsson BJ, Yang J, Finucane HK, Gusev A, Lindstr\u0026ouml;m S, Ripke S, et al. Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores. The American Journal of Human Genetics. 2015;97:576\u0026ndash;592.\u003c/li\u003e\n\u003cli\u003eKunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, et al. Genetic meta-analysis of diagnosed Alzheimer\u0026rsquo;s disease identifies new risk loci and implicates A\u0026beta;, tau, immunity and lipid processing. Nat Genet. 2019;51:414\u0026ndash;430.\u003c/li\u003e\n\u003cli\u003eTank R, Ward J, Flegal KE, Smith DJ, Bailey MES, Cavanagh J, et al. Association between polygenic risk for Alzheimer\u0026rsquo;s disease, brain structure and cognitive abilities in UK Biobank. Neuropsychopharmacology 2021 47:2. 2021;47:564\u0026ndash;569.\u003c/li\u003e\n\u003cli\u003eSinnott-Armstrong N, Tanigawa Y, Amar D, Mars N, Benner C, Aguirre M, et al. Genetics of 35 blood and urine biomarkers in the UK Biobank. Nature Genetics 2021 53:2. 2021;53:185\u0026ndash;194.\u003c/li\u003e\n\u003cli\u003eWelsh C, Celis-Morales CA, Brown R, Mackay DF, Lewsey J, Mark PB, et al. Comparison of Conventional Lipoprotein Tests and Apolipoproteins in the Prediction of Cardiovascular Disease. Circulation. 2019. 2019. https://doi.org/10.1161/circulationaha.119.041149.\u003c/li\u003e\n\u003cli\u003eEisenberg DTA, Kuzawa CW, Hayes MG. Worldwide allele frequencies of the human apolipoprotein E gene: climate, local adaptations, and evolutionary history. Am J Phys Anthropol. 2010;143:100\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eDalecki M, Willits FK. Examining change using regression analysis: Three approaches compared. Sociological Spectrum. 1991;11:127\u0026ndash;145.\u003c/li\u003e\n\u003cli\u003eJareebi M, Fullerton J, Kancheva A, Tank R, Gilchrist L, Russell E, et al. Associations and interactions between premorbid cognitive health, apolipoprotein e4 genotype, and incident Alzheimer\u0026rsquo;s disease in UK Biobank (N=252,340). 2024. 26 November 2024. https://doi.org/10.31219/OSF.IO/RJTZX.\u003c/li\u003e\n\u003cli\u003ePicard C, Nilsson N, Labont\u0026eacute; A, Auld D, Rosa-Neto P, Ashton NJ, et al. Apolipoprotein B is a novel marker for early tau pathology in Alzheimer\u0026rsquo;s disease. Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2022;18:875\u0026ndash;887.\u003c/li\u003e\n\u003cli\u003eSafieh M, Korczyn AD, Michaelson DM. ApoE4: an emerging therapeutic target for Alzheimer\u0026rsquo;s disease. BMC Med. 2019;17:64.\u003c/li\u003e\n\u003cli\u003eSuri S, Heise V, Trachtenberg AJ, Mackay CE. The forgotten APOE allele: a review of the evidence and suggested mechanisms for the protective effect of APOE ɛ2. Neurosci Biobehav Rev. 2013;37:2878\u0026ndash;2886.\u003c/li\u003e\n\u003cli\u003eDeary IJ, Whiteman MC, Pattie A, Starr JM, Hayward C, Wright AF, et al. Apolipoprotein e gene variability and cognitive functions at age 79: a follow-up of the Scottish mental survey of 1932. Psychol Aging. 2004;19:367\u0026ndash;371.\u003c/li\u003e\n\u003cli\u003eMartin L, Boutwell BB, Messerlian C, Adams CD. Mendelian randomization reveals apolipoprotein B shortens healthspan and possibly increases risk for Alzheimer\u0026rsquo;s disease. Communications Biology 2024 7:1. 2024;7:1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eGong J, Harris K, Peters SAE, Woodward M. Serum lipid traits and the risk of dementia: A cohort study of 254,575 women and 214,891 men in the UK Biobank. EClinicalMedicine. 2022;54.\u003c/li\u003e\n\u003cli\u003eMartin L, Boutwell BB, Messerlian C, Adams CD. Mendelian randomization reveals apolipoprotein B shortens healthspan and possibly increases risk for Alzheimer\u0026rsquo;s disease. Communications Biology 2024 7:1. 2024;7:1\u0026ndash;12.\u003c/li\u003e\n\u003cli\u003eWardlaw JM, Makin SJ, Vald\u0026eacute;s Hern\u0026aacute;ndez MC, Armitage PA, Heye AK, Chappell FM, et al. Blood-brain barrier failure as a core mechanism in cerebral small vessel disease and dementia: evidence from a cohort study. Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2017;13:634.\u003c/li\u003e\n\u003cli\u003eBarnes-V\u0026eacute;lez JA, Aksoy Yasar FB, Hu J. Myelin lipid metabolism and its role in myelination and myelin maintenance. The Innovation. 2023;4:100360.\u003c/li\u003e\n\u003cli\u003eJohannesen CDL, Langsted A, Nordestgaard BG, Mortensen MB. Excess Apolipoprotein B and Cardiovascular Risk in Women and Men. J Am Coll Cardiol. 2024;83:2262\u0026ndash;2273.\u003c/li\u003e\n\u003cli\u003eKhan TA, Shah T, Prieto D, Zhang W, Price J, Fowkes GR, et al. Apolipoprotein E genotype, cardiovascular biomarkers and risk of stroke: Systematic review and meta-analysis of 14 015 stroke cases and pooled analysis of primary biomarker data from up to 60 883 individuals. Int J Epidemiol. 2013;42:475\u0026ndash;492.\u003c/li\u003e\n\u003cli\u003eMaxwell SS, Jackson CA, Paternoster L, Cordonnier C, Thijs V, Al-Shahi Salman R, et al. Genetic associations with brain microbleeds Systematic review and meta-analyses. Neurology. 2011;77:158\u0026ndash;167.\u003c/li\u003e\n\u003cli\u003eHolmes M V., Asselbergs FW, Palmer TM, Drenos F, Lanktree MB, Nelson CP, et al. Mendelian randomization of blood lipids for coronary heart disease. Eur Heart J. 2015;36:539\u0026ndash;550.\u003c/li\u003e\n\u003cli\u003eLivingston G, Huntley J, Liu KY, Costafreda SG, Selb\u0026aelig;k G, Alladi S, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. The Lancet. 2024;404:572\u0026ndash;628.\u003c/li\u003e\n\u003cli\u003eRemaley AT, Cole J, Sniderman AD. ApoB Is Ready for Prime Time. J Am Coll Cardiol. 2024;83:2274\u0026ndash;2275.\u003c/li\u003e\n\u003cli\u003eNg TKS, Beck T, Liu X, Desai P, Holland T, Dhana K, et al. Longitudinal Associations between Lipid Panel and Cognitive Decline Modified by APOE 4 Carrier Status in Biracial Community-dwelling Older Adults: Findings from the Chicago Health and Aging Project. Arch Gerontol Geriatr. 2025:105825.\u003c/li\u003e\n\u003cli\u003eWang Y, Grydeland H, Roe JM, Pan M, Magnussen F, Amlien IK, et al. Associations of circulating C-reactive proteins, APOE \u0026epsilon;4, and brain markers for Alzheimer\u0026rsquo;s disease in healthy samples across the lifespan. Brain Behav Immun. 2022;100:243\u0026ndash;253.\u003c/li\u003e\n\u003cli\u003eSubramaniapillai S, Schindler LS, Redmond P, Bastin ME, Wardlaw JM, Vald\u0026eacute;s Hern\u0026aacute;ndez M, et al. Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study. Neurology. 2024;103.\u003c/li\u003e\n\u003cli\u003eKivim\u0026auml;ki M, Luukkonen R, Batty GD, Ferrie JE, Pentti J, Nyberg ST, et al. Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals. Alzheimers Dement. 2018;14:601\u0026ndash;609.\u003c/li\u003e\n\u003cli\u003eCox SR. Neurocognitive Aging. Annu Rev Dev Psychol. 2024;6:505\u0026ndash;527.\u003c/li\u003e\n\u003cli\u003eTucker-Drob EM. Cognitive Aging and Dementia: A Life-Span Perspective. Annu Rev Dev Psychol. 2019;1:177\u0026ndash;196.\u003c/li\u003e\n\u003cli\u003eLyall DM, Cullen B, Allerhand M, Smith DJ, Mackay D, Evans J, et al. Cognitive test scores in UK biobank: Data reduction in 480,416 participants and longitudinal stability in 20,346 participants. PLoS One. 2016;11.\u003c/li\u003e\n\u003cli\u003eLyall DM, Quinn T, Lyall LM, Ward J, Anderson JJ, Smith DJ, et al. Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample. Brain Commun. 2022. 9 May 2022. https://doi.org/10.1093/BRAINCOMMS/FCAC119.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: linear regression estimates of change in blood biomarkers, by apolipoprotein e4 presence (versus e3e3)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 356px;\"\u003e\n \u003cp\u003ePartially-adjusted model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003eFully-adjusted model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eLower 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eUpper 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eLower 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eUpper 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eIGF-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.505\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.652\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.357\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12325\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.508\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.655\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.361\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12236\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eVitamin D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e11419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e11339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTestosterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLipoprotein A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHbA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRandom Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-2.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCystatin-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eApoB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12297\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12209\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eApoA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCreatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.055\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.088\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12288\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAspartate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.066\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12317\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.074\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12228\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlkaline phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e12372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e12283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e95% CI = 95% confidence intervals. Significant P\u0026lt;0.002 highlighted in bold. CRP = C reactive Protein. IGF-1 = insulin-like growth factor 1. SHGB = Sex hormone binding globulin. Hba1C = glycated haemoglobin. H/LDL = high/low density lipoproteins. Partially adjusted: controlling for age at baseline, sex, 10 principal components for stratification, timepoint interval, and respective baseline biomarker values. Fully adjusted: additionally controlling for Townsend, medication at baseline and ever-smoking status. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: linear regression estimates of change in blood biomarkers, by apolipoprotein e2 presence (versus e3e3)\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 356px;\"\u003e\n \u003cp\u003ePartially-adjusted model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cp\u003eFully-adjusted model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003eLower 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eUpper 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eLower 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eUpper 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eIGF-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eVitamin D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e9735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-1.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e9653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eProtein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTestosterone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003ePhosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eLipoprotein A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-1.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHbA1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eRandom Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-1.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-1.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCystatin-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eApoB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.059\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10307\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.054\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10219\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eApoA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCreatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.155\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.112\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10388\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.172\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.215\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.129\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10300\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e7887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAspartate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10223\u003c/p\u003e\n \u003c/td\u003e\n 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\u003cp\u003e\u003cstrong\u003e-0.186\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.119\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10336\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.168\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.201\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.135\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10248\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlkaline phosphate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e10385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 70px;\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 54px;\"\u003e\n \u003cp\u003e8400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e95% CI = 95% confidence intervals. Significant P\u0026lt;0.002 highlighted in bold. CRP = C reactive Protein. IGF-1 = insulin-like growth factor 1. SHGB = Sex hormone binding globulin. Hba1C = glycated haemoglobin. H/LDL = high/low density lipoproteins. Partially adjusted: controlling for age at baseline, sex, 10 principal components for stratification, timepoint interval, and respective baseline biomarker values. Fully adjusted: additionally controlling for Townsend, medication at baseline and ever-smoking status.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"UK Biobank, biomarkers, dementia, longitudinal, Apolipoprotein b","lastPublishedDoi":"10.21203/rs.3.rs-6802545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6802545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding pathways from genetic variation to cognitive impairment is critical for dementia prevention, risk stratification and the development of treatments. While genetic risk factors for dementia are known to associate with cross-sectional differences in biomarkers (e.g. lipids) in healthy people, potential influence over longitudinal trajectories is not understood.\u003c/p\u003e\u003cp\u003eWe leveraged genetic, general health and two-wave biomarker data from n\u0026thinsp;=\u0026thinsp;17,817 UK Biobank participants. The outcomes were change in 26 common circulating blood biomarkers including inflammatory, cardiometabolic and lipid families. The presence of apolipoprotein (\u003cem\u003eAPOE\u003c/em\u003e) e4 \u0026lsquo;risk\u0026rsquo; and e2 \u0026lsquo;protective\u0026rsquo; alleles were tested separately versus \u0026lsquo;neutral\u0026rsquo; e3e3 genotype, as were associations of non-\u003cem\u003eAPOE\u003c/em\u003e polygenic risk for Alzheimer\u0026rsquo;s disease. Biomarker change values were corrected for baseline levels, age, deprivation, sex, timepoint interval, smoking history, medication history, deprivation, genotyping chip and 10 genetic principal components (fully-adjusted).\u003c/p\u003e\u003cp\u003eThe average interval between assessments was 4.30 years (standard deviation; SD\u0026thinsp;=\u0026thinsp;0.92). For e4 (versus e3e3), four associations were significant: accelerated change in total cholesterol, apolipoprotein b (ApoB) and low-density lipoprotein (LDL) each in the direction of poorer health (standardized β range\u0026thinsp;=\u0026thinsp;0.021 SDs to 0.036 more change relative to e3e3), and c-reactive protein protectively (β = -0.059; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For e2 allele presence, there were three significant associations: change in ApoB, total cholesterol and LDL in protective directions (β range = -0.057 to -0.090). There were no \u003cem\u003eAPOE\u003c/em\u003e genotypic interactions with baseline age, sex, or medication history, nor significant findings associated with non-\u003cem\u003eAPOE\u003c/em\u003e (Alzheimer\u0026rsquo;s disease) polygenic risk.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAPOE\u003c/em\u003e e genotype significantly modifies particularly lipid trajectories across time \u0026ndash; most strongly ApoB levels. This adds nuance to lipids as a dementia risk factor, and, clinically, suggests more frequent lipid assessments in e4 carriers in that context. Our findings provide a plausible partial biological explanation for \u003cem\u003eAPOE\u0026rsquo;s\u003c/em\u003e progressive influence on neurocognitive health.\u003c/p\u003e","manuscriptTitle":"Genetic risk for Alzheimer’s disease, and differential trajectories in circulating blood biomarkers in UK Biobank (n=17,817)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:49:43","doi":"10.21203/rs.3.rs-6802545/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43f7ee49-ea73-4e20-be63-40c024d6729d","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52691075,"name":"Health sciences/Medical research/Genetics research"},{"id":52691076,"name":"Biological sciences/Genetics/Genetic markers"},{"id":52691077,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-08-25T20:05:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 05:49:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6802545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6802545","identity":"rs-6802545","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}