key: cord-0315087-kesldtyl authors: Goudswaard, L. J.; Corbin, L. J.; Burley, K. L.; Mumford, A.; Akbari, P.; Soranzo, N.; Butterworth, A. S.; Watkins, N. A.; Pournaras, D. J.; Harris, J.; Timpson, N. J.; Hers, I. title: Higher body mass index raises immature platelet count: evidence from Mendelian randomization analyses date: 2021-05-20 journal: nan DOI: 10.1101/2021.05.19.21257443 sha: 920281e9588716b2322162d41469dab43af9f955 doc_id: 315087 cord_uid: kesldtyl A higher body mass index (BMI) is a recognised risk factor for thrombosis. Platelets are essential for haemostasis but also contribute to thrombosis when activated pathologically. We hypothesised that an increase in BMI may lead to changes in platelet characteristics, thereby contributing to increased thrombotic risk. The effect of BMI on platelet traits measured by Sysmex XN-1000 was explored in 33388 UK blood donors from the INTERVAL study. Linear regression was used for observational analyses between BMI and platelet characteristics. Mendelian randomization (MR) was used to estimate a causal effect with BMI proxied by a genetic risk score. Follow-up analysis explored the relevance of platelet characteristics on whole blood platelet aggregation in a pre-operative cardiac cohort (COPTIC) using linear regression. Observationally, higher BMI was positively associated with greater plateletcrit (PCT), platelet count (PLT), immature platelet count (IPC) and side fluorescence (SFL, a measure of mRNA content used to derive IPC). MR provided causal estimates for a positive effect of BMI on both SFL and IPC (IPC 0.06 SDs higher per SD higher BMI, 95% CI 0.006 to 0.12, P=0.03), but there was no strong evidence for a causal effect of BMI on PCT or PLT. The COPTIC study provided observational evidence for a positive association between IPC and whole blood platelet aggregation induced by adrenaline, TRAP-6 and ADP. Our results indicate that higher BMI raises the number of immature platelets, which is associated with greater whole blood platelet aggregation. Higher IPC could therefore contribute to obesity-related thrombosis. 7 approaches to test the hypothesis that higher BMI leads to changes in platelet characteristics. Although 146 observational studies can demonstrate associations between BMI and platelet characteristics, they estimation of the causal effect of BMI on platelet traits, reducing the effect of confounding factors that are 150 inherent to observational studies. To assess functional implications of BMI-platelet associations, a follow-151 up analysis was designed to explore the associations between platelet characteristics and whole blood 152 aggregation in a cohort of cardiac surgery patients. (N=33388) 177 178 covariables used in the analysis were age, sex, smoking status (in three categories of never, previous 183 and current) and alcohol consumption (in four categories of rarely, less than once a week, 1-2 times a 184 week and 3-5 times a week or most days). These covariables were chosen due to their plausible . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 associations with both BMI and cardiovascular health [2] , therefore it is important to adjust for these 186 variables in the observational estimates. Table 1 . These data were rank normal transformed to normalize the distribution of each trait. Therefore, each platelet index is measured in normalized standard deviation (SD) units. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 20, 2021. To understand properties of the GRS for BMI, the association between the GRS with both BMI and 233 covariables were explored. The MR analysis was performed by using a two-stage least squares (2SLS) 234 regression model (using systemfit() function from "systemfit" package [31], Error! Reference source not 235 found.). The MR causal estimates reflect the change in platelet traits (in SD units) per SD increase in 236 BMI. A Wu-Hausman test was performed to test for endogeneity between observational and MR 237 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. The Sysmex XN-1000 haematology analyzer measures multiple platelet traits, however many of these 299 traits are closely related measurements and therefore may not be completely independent. Indeed, 300 platelet traits showed a high degree of correlation with each other (), in particular among similar 301 measures. For example, measures of PLT (PLT I/F) and PCT, the latter a measurement of platelet mass, were highly positively correlated with each other but were weakly inversely correlated with other platelet 303 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101/2021.05.19.21257443 doi: medRxiv preprint platelet count (PLT (I) 0.11 SD higher per SD higher BMI, 95% CI 0.09 to 0.12, P = 1.0 x 10 -67 ). The next 332 strongest association with BMI was the association with SFL (0.06 SD higher per SD increase in BMI, 333 95% CI 0.05 to 0.07, P = 4.7x10 -23 ) . BMI also showed a positive association with IPC (0.06 SD higher per 334 SD increase in BMI, 95% CI 0.05 to 0.08, P = 4.8x10 -22 ) . These results demonstrate that BMI is positively 335 associated with PCT, PLT (I), SFL and IPC in this population. These estimates were very similar to the 336 age and sex only adjusted estimates (Supp Table 1 ). In the MR analyses, BMI was associated with fewer traits than in the observational analysis (Supp Table 378 7, Figure 4) . The causal estimate for the effect of BMI on SFL was 0.08 SDs per SD increase in BMI 379 (95% CI 0.03 to 0.14, P = 0.003). This estimate was of larger magnitude than the observational estimate. The causal estimate for BMI and IPC was 0.06 SDs per SD increase in BMI (95% CI 0.006 to 0.12, P = 381 0.03), a similar magnitude of effect to the observational estimate. In the MR analysis, unlike in the . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101/2021.05.19.21257443 doi: medRxiv preprint platelet traits. Across all but one (H-IPF) of the platelet phenotypes we did not observe differences in 386 directionality of effect comparing observational effects and causal effects predicted by Mendelian 387 randomization (Figure 4) . The Wu-Hausman test suggested that observational and MR estimates were similar for the majority of 389 platelet traits (P > 0.05), except for measures of platelet count and plateletcrit (P < 0.001) (Supp Table 7 ). Given evidence for a causal effect of BMI on IPC in the MR analysis, we sought to evaluate the 402 relationship between IPC and platelet activity as a biological parameter of clinical relevance. Whilst this 403 analysis could not be conducted in INTERVAL due to a lack of suitable data, we were able to utilize data 404 from the COPTIC study to address this question. The COPTIC study is a cohort of cardiac surgery 405 patients, with samples taken pre-surgery. These participants have whole blood aggregation measured, 406 therefore making it possible to determine associations between IPC and aggregation in a clinical setting. The total number of COPTIC participants was 2541. Of these, 2518 participants gave consent for future 410 research ( Table 5) . Participants included in the analysis were those not on antiplatelet therapy (N=655). The majority of participants were male (61.8%), with a mean age of 63.9 years (SD of 16.1). Similar to the 412 INTERVAL cohort, the mean BMI was in the overweight category (27.2 kg/m 2 with a SD of 4.9 kg/m 2 ). The majority of participants were either never smokers or ex-smokers for more than 5 years (89.2 %). To determine the potential functional effects of variation in IPC, the COPTIC cohort was used to assess 421 the observational association between IPC and whole blood platelet aggregation in response to a range 422 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 regimes to ensure sufficient platelet inhibition [17] . Observational studies have also found that patients 495 with COVID-19 have elevated immature platelet counts and fraction, which could partly explain high rates 496 COVID-19 induced thrombosis [35, 36] . Despite the association between BMI and IPC, there was less 497 evidence for an effect of BMI on IPF. This lack of association may be because IPF is the proportion of 498 immature platelets, therefore if someone had a higher number of immature platelets, but also a higher 499 number of platelets overall (for example due to increased platelet lifespan), there would be no increase in 500 the immature platelet fraction. association [19, 20, 22] . The results of the current study suggest that associations seen previously 517 between BMI and MPV are likely due to confounding of observational estimates. The lack of association 518 . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 immature platelets may not affect the overall median size of the whole platelet population. Although this study suggests potential effects of BMI on platelet traits such as SFL and IPC, it does not 525 provide mechanistic insight into how BMI exerts these effects. Previous studies have suggested that 526 inflammation driven by adiposity can stimulate megakaryocyte proliferation, thereby increasing platelet 527 numbers [20] . There is evidence that inflammatory mediators such as interleukin-6 (IL-6) could be one 528 such factor [37] . Further study would be warranted to explore these mechanisms, as well as replicate the 529 current findings, such as through independent population or clinical studies. There are a few limitations to the study that should be recognised. Firstly, BMI was derived from self-532 reported height and weight. Although there is potential for this to bias observations, previous studies have 533 found that self-reported BMI and BMI measured in the clinic are strongly associated [38] . The GRS also 534 associates with BMI to the extent expected. Secondly, there may be other confounders which were not 535 recorded within INTERVAL and therefore could not be accounted for in our models, such as, socio-536 economic position, which may affect both BMI and risk of thrombosis. Therefore, residual confounding of 537 observational estimates cannot be ruled out. With respect to the observational analysis conducted in 538 COPTIC, the sample size is modest, which may limit power to detect associations. Furthermore, this 539 cohort required cardiac surgery and therefore it is possible that associations found may not be 540 generalizable to the wider population. However, these findings do indicate that immature platelets may be 541 a biomarker of platelet hyperactivity in patients with a history of cardiovascular disease. Altogether, we show observational and MR evidence that an increased BMI is associated with an 544 increase in number of immature platelets. Observational evidence indicates that higher immature platelet 545 count is associated with enhanced aggregation in a cardiac surgery cohort. Together, these results . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 indicate that higher BMI may enhance platelet function and thrombosis by increasing platelet production 547 and immature platelet count. was involved in study design. JH was involved in the conduct, data analysis and linkage of the COPTIC 553 study. NJT and IH were involved in concept and design of the study and revising the intellectual content. CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 20, 2021. . CC-BY 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 20, 2021. ; https://doi.org/10.1101 https://doi.org/10. /2021 GPVI surface expression and signalling pathway activation are increased 9 Body Mass Index and Platelet Reactivity During Dual Antiplatelet Therapy With 10 Platelet aggregation is dependent on platelet count in patients with coronary 609 artery disease Mean platelet volume may represent a predictive parameter for overall vascular 611 mortality and ischemic heart disease Genetically Determined Platelet Count and Risk of Cardiovascular Disease Immature Platelet Count Levels as a Novel Quality Marker in Plateletpheresis Association of immature platelets with adverse cardiovascular outcomes Immature Platelets: Clinical Relevance and Research Perspectives Impact of immature platelets on platelet response to ticagrelor and 621 prasugrel in patients with acute coronary syndrome Immature platelet fraction (IPF) determined with an automated method predicts 623 clopidogrel hyporesponsiveness How obesity affects the neutrophil/lymphocyte and platelet/lymphocyte 21 The mean platelet volume in patients with obesity Severe obesity and bariatric surgery alter the platelet mRNA profile Platelet reactivity and response to aspirin in subjects with the metabolic syndrome Reticulated platelets and antiplatelet therapy response in diabetic patients Efficiency and safety of varying the frequency of whole blood donation 638 (INTERVAL): a randomised trial of 45 000 donors The Allelic Landscape of Human Blood Cell Trait Variation and Links to 27 Genetic Analyses of Blood Cell Structure for Biological and Pharmacological 28 Interleukin-6 stimulates thrombopoiesis through thrombopoietin: role in 38 Participants in the INTERVAL randomised controlled trial were recruited with the active collaboration of 557 NHS Blood and Transplant England (www.nhsbt.nhs.uk), which has supported field work and other