10.4081/btvb.2023.83
Platelet distribution width (PDW), a marker of platelet size heterogeneity used as a readout of processes leading to platelet production and destruction, was recently reported to tag platelet activation variability. As platelets participate in the pathogenesis of many acute and chronic diseases, we evaluated PDW as a predictor of all-cause and cause-specific mortality. Longitudinal analysis was performed on 17,334 participants (52% women, mean age 55.6±12 years) in the Moli-sani study cohort, without a history of hematological diseases. Baseline PDW measurements were categorized in tertiles, the lowest acting as the reference. A multivariable Cox-proportional hazard model was used to estimate the association between PDW and mortality. Over a median follow-up of 11.6 years (interquartile range 10.7-12.5), 1,535 deaths [37.7% cardiovascular disease (CVD) and 36.5% cancer] were ascertained. As compared to those in the first PDW tertile (14.6-16.0 fL), individuals within the highest tertile (16.6-20.4 fL) had an increased risk of all-cause [hazard ratios (HR):1.20; 95% CI: 1.04-1.37] and CVD mortality (HR:1.29; 1.03-1.62). No association between PDW and cancer mortality was found in the whole sample. Subgroup analyses by two age classes (35-65y, ≥65y) showed that the association of PDW with both all-cause and cancer mortality was more apparent in the elderly (HR:1.34; 1.14-1.58, P for interaction =0.028 and HR:1.37; 1.01-1.85, P for interaction =0.020, respectively). We conclude that PDW-associated increase in CVD mortality risk could be related to accelerated/altered activation, production, or destruction of platelets, leading to several clinical conditions and death. In the elderly, PDW involvement in all-cause and cancer mortality should be further investigated.
 
 
*Moli-sani investigatorsSteering committee: Licia Iacoviello, Giovanni de Gaetano, Maria Benedetta Donati. Scientific secretariat: Marialaura Bonaccio, Americo Bonanni, Chiara Cerletti, Simona Costanzo, Amalia De Curtis, Augusto Di Castelnuovo, Alessandro Gialluisi, Francesco Gianfagna, Mariarosaria Persichillo, Teresa Di Prospero. Safety and ethical committee: Jos Vermylen, Renzo Pegoraro, Antonio Spagnolo. External event adjudicating committee: Deodato Assanelli, Livia Rago. Baseline and follow-up data management: Simona Costanzo, Marco Olivieri, Teresa Panzera. Data analysis: Augusto Di Castelnuovo, Marialaura Bonaccio, Simona Costanzo, Simona Esposito, Alessandro Gialluisi, Francesco Gianfagna, Sabatino Orlandi, Emilia Ruggiero, Alfonsina Tirozzi. Biobank, molecular and genetic laboratory: Amalia De Curtis, Sara Magnacca, Fabrizia Noro, Alfonsina Tirozzi. Recruitment staff: Mariarosaria Persichillo, Francesca Bracone, Teresa Panzera. Communication and press office: Americo Bonanni. Regional institutions: Direzione Generale per la Salute - Regione Molise; Azienda Sanitaria Regionale del Molise; Agenzia Regionale per la Protezione Ambientale del Molise; Molise Dati Spa; Offices of vital statistics of the Molise region. Hospitals: Presidi Ospedalieri ASReM: Ospedale A. Cardarelli, Campobasso; Ospedale F. Veneziale, Isernia; Ospedale San Timoteo, Termoli (CB); Ospedale Ss. Rosario, Venafro (IS); Ospedale Vietri, Larino (CB); Ospedale San Francesco Caracciolo, Agnone (IS); Casa di Cura Villa Maria, Campobasso; Ospedale Gemelli Molise, Campobasso; IRCCS Neuromed, Pozzilli (IS), Italy.
application/pdf
2785-5309
83
3
en
*on behalf of the Moli-sani Study Investigators
https://creativecommons.org/licenses/by-nc/4.0
Bleeding, Thrombosis and Vascular Biology
prospective cohort study
Platelet distribution width is associated with cardiovascular mortality in an adult general population
Text.Serial.Journal
https://www.btvb.org/btvb
2
Original Articles
2023-09-27
2023-05-12
2023-07-31
2023-11-15
Bleed Thromb Vascul Biol
*on behalf of the Moli-sani Study Investigators
2023/09/27
10.4081/btvb.2023.83
Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli (IS), Italy; EPIMED Research Center, Department of Medicine and Surgery, University of Insubria, Varese, Italy
2785-5309
3
prospective cohort study
en
Briggs C. Quality counts: new parameters in blood cell counting. Int J Lab Hematol 2009;31:277-97.
Platelet distribution width is associated with cardiovascular mortality in an adult general population
https://www.btvb.org/btvb/article/download/83/83
2
                                                                                                            ORIGINAL ARTICLE

Correspondence: Benedetta Izzi, Centro Nacional de Investiga-               Platelet distribution width is
ciones Cardiovasculares Carlos III (F.S.P.), C/ Melchor Fernández
Almagro, 3, 28029 Madrid, España.
Tel.: +34.914531200. (Ext.: 1157).
                                                                            associated with cardiovascular
E-mail: izzi.epigenomica@gmail.com
                                                                            mortality in an adult general
Citation: Izzi B, Costanzo S, Gialluisi A, et al. Platelet distribution
width is associated with cardiovascular mortality in an adult gen-          population
eral population. Bleeding, Thrombosis, and Vascular Biology
2023;2:83.
                                                                            Benedetta Izzi,1 Simona Costanzo,1 Alessandro Gialluisi,1,2
Acknowledgments: a part of the Graphical Abstract (Figure 4) was
drawn by using pictures from Servier Medical Art. Servier Medical           Amalia De Curtis,1 Sara Magnacca,3 Teresa Panzera,1
Art by Servier is licensed under a Creative Commons Attribution 3.0         Augusto Di Castelnuovo,3 Maria Benedetta Donati,1
Unported License (https://creativecommons.org/licenses/by/3.0/).
                                                                            Chiara Cerletti,1 Marc F Hoylaerts,4 Giovanni de Gaetano,1
Key words: platelet distribution width, CVD mortality, all-cause            Licia Iacoviello1,2
mortality, cancer mortality, prospective cohort study.
                                                                            on behalf of the Moli-sani Study Investigators
Contributions: BI and SC equally contributed to the manuscript.
BI, SC, designed the study and wrote the manuscript; SC, per-               1
                                                                             Department of Epidemiology and Prevention, IRCCS
formed the statistical analysis; SC, ADeC, SM, TP, managed the
data collection and the laboratory tests, AG, ADiC, MBD, CC,                NEUROMED, Pozzilli (IS), Italy; 2EPIMED Research Center,
MFH, GdeG, LI, contributed to concepts and reviewed the manu-               Department of Medicine and Surgery, University of Insubria,
script; ADiC, MBD, CC, GdeG, LI, were at the origin of the con-             Varese, Italy; 3Mediterranea Cardiocentro, Napoli, Italy; 4Center




                                                                                              ly
ception of the Moli-sani cohort. All the authors approved the final
version to be published.
                                                                            for Molecular and Vascular Biology, Department of




                                                                                      on
                                                                            Cardiovascular Sciences, University of Leuven, Leuven, Belgium
Ethical approval and consent to participate: the Moli-sani study
complies with the Declaration of Helsinki and was approved by
the Catholic University Ethical Committee, Rome, Italy. All par-




                                                                                e
ticipants enrolled provided written informed consent.
                                                                            ABSTRACT
Availability of data and material: the data underlying this article
will be shared upon reasonable request by the corresponding au-
thor. The data are stored in an institutional repository (https://repos-
                                                                           us    Platelet distribution width (PDW), a marker of platelet size
                                                                            heterogeneity used as a readout of processes leading to platelet
itory.neuromed.it) and access is restricted by the ethical approvals        production and destruction, was recently reported to tag platelet
                                                                           al
and the legislation of the European Union.                                  activation variability. As platelets participate in the pathogenesis
                                                       ci

Conflict of interest: the authors declare no potential conflict of in-      of many acute and chronic diseases, we evaluated PDW as a pre-
terest.                                                                     dictor of all-cause and cause-specific mortality. Longitudinal
                                                     er


                                                                            analysis was performed on 17,334 participants (52% women,
Funding: the enrollment phase of the Moli-sani Study was sup-               mean age 55.6±12 years) in the Moli-sani study cohort, without
ported by unrestricted research grants from the Pfizer Foundation
                                                    m




(Rome, Italy), the Italian Ministry of University and Research              a history of hematological diseases. Baseline PDW measurements
(MIUR, Rome, Italy) – Programma Triennale di Ricerca, Decreto               were categorized in tertiles, the lowest acting as the reference. A
                                         om




no.1588 and Instrumentation Laboratory, Milan, Italy. Laboratory            multivariable Cox-proportional hazard model was used to esti-
analyses of the Moli-sani study were partially supported by Bio-
marCaRE (Biomarkers for Cardiovascular Risk Assessment in Eu-               mate the association between PDW and mortality. Over a median
rope): European Commission Seventh Framework Programme                      follow-up of 11.6 years (interquartile range 10.7-12.5), 1,535
                                  -c




FP7/2007-2013 (HEALTH-F2-2011-278913) (L.I.). The present                   deaths [37.7% cardiovascular disease (CVD) and 36.5% cancer]
work was also partially funded by the European Union’s Horizon              were ascertained. As compared to those in the first PDW tertile
2020 research and innovation programme under the Marie
                         on




Sklodowska-Curie (grant agreement No 798841 to B.I) and the                 (14.6-16.0 fL), individuals within the highest tertile (16.6-20.4
Italian Ministry of Health (GR 2018-12366528 to B.I. and A.G.               fL) had an increased risk of all-cause [hazard ratios (HR):1.20;
and Ricerca Corrente 2022-2024). Fibrinogen levels in the Moli-             95% CI: 1.04-1.37] and CVD mortality (HR:1.29; 1.03-1.62). No
                   N




sani cohort were measured within a collaborative research project
with the Department of Functional Coagulation, Synapse Research
                                                                            association between PDW and cancer mortality was found in the
Institute, Maastricht, the Netherlands. Funders had no role in study        whole sample. Subgroup analyses by two age classes (35-65y,
design; collection, analysis, or interpretation of data, the writing        ≥65y) showed that the association of PDW with both all-cause
of the manuscript, or the decision to submit the article for publica-       and cancer mortality was more apparent in the elderly (HR:1.34;
tion.
                                                                            1.14-1.58, P for interaction =0.028 and HR:1.37; 1.01-1.85, P for
Received: 12 May 2023.                                                      interaction =0.020, respectively). We conclude that PDW-associ-
Accepted: 31 August 2023.                                                   ated increase in CVD mortality risk could be related to acceler-
Publisher’s note: all claims expressed in this article are solely those
                                                                            ated/altered activation, production, or destruction of platelets,
of the authors and do not necessarily represent those of their affili-      leading to several clinical conditions and death. In the elderly,
ated organizations, or those of the publisher, the editors and the re-      PDW involvement in all-cause and cancer mortality should be
viewers. Any product that may be evaluated in this article or claim         further investigated.
that may be made by its manufacturer is not guaranteed or endorsed
by the publisher.
©
 Copyright: the Author(s), 2023
Licensee PAGEPress, Italy                                                   Introduction
Bleeding, Thrombosis and Vascular Biology 2023; 2:83
doi:10.4081/btvb.2023.83                                                        Number and volume changes of blood platelets have long
This work is licensed under a Creative Commons Attribution-                 been considered as important determinants of pathophysiologi-
NonCommercial 4.0 International License (CC BY-NC 4.0).                     cal processes including athero-thrombosis and thrombo-inflam-
                                                                            mation, leading causes of cardiovascular disease. Platelet count


                                             Bleeding, Thrombosis and Vascular Biology 2023; 2:83
20                                                              B. Izzi et al.




(Plt) and two platelet size indices, namely mean platelet volume           55.6±12.1 years, Supplementary Figure 1]. Details on blood and
(MPV) and platelet distribution width (PDW), are often avail-              biochemical parameters measured in the cohort and covariate
able in routine blood analysis and are used as diagnostic support          assessment for the present study are reported in the supplemen-
in several clinical settings. Both Plt and MPV have been largely           tary material.
investigated in relation to mortality in epidemiological studies
focusing on general populations. Low and/or high Plt has been              Assessment of cause-specific and total mortality
associated with increased all-cause, cardiovascular disease
(CVD) or cancer mortality risk.1-8 Data on the relationship be-                 The Moli-sani Study cohort was followed up for mortality
tween MPV and mortality are more controversial and less con-               until December 31st, 2018. Cause-specific mortality was assessed
sistent across studies: both increased and decreased mortality             by the Italian mortality registry (ReNCaM; Registro Nominativo
risk has been reported in association with MPV.9-11                        delle Cause di Morte), validated by Italian death certificates
     Contrary to Plt and MPV, PDW was never investigated as a              (ISTAT form), and coded according to the International Classifi-
marker of mortality risk in the context of general population              cation of Diseases (ICD-9).
studies, though in an Israeli cohort of 1,036 in-ward patients (age             CVD mortality included deaths from diseases of the circula-
66.4±18 years, 58.3% men) not selected for any specific patho-             tory system when the underlying cause of death included ICD-9
logical condition, higher PDW was associated with an increased             codes 390-459. Cancer death was considered when the underlying
risk of 90-day mortality and shortened survival after discharge            cause of death included ICD-9 codes 140-208. Non cardiovascu-
from the hospital.12                                                       lar/non-cancer causes of death were included in the ‘other cause




                                                                                                      ly
     Reflecting platelet size heterogeneity,13-16 PDW is frequently        mortality’ group.




                                                                                              on
used as a diagnostic marker for diseases with impaired
megakaryocyte differentiation and thrombopoiesis and identified            Statistical analysis
as a prognosis predictor in clinically-defined populations with
                                                                                The normality of continuous variables was assessed by the




                                                                                        e
several inflammatory-based conditions including CVD, cancer,
                                                                           Shapiro-Wilk test and confirmed graphically. Normally distrib-
metabolic syndrome, diabetes, and neurological disorders.17-26
Using an observational approach we have recently shown in the
Moli-family cohort that PDW specifically and significantly re-
                                                                                 us
                                                                           uted data are presented as mean [SD or standard error (SE)],
                                                                           skewed as geometric mean (95% CI), and categorical variables
                                                                           as frequencies. PDW was classified into tertiles. The difference
flects ex vivo and in vitro P-selectin-dependent platelet activa-
                                                                         al
                                                                           in the distribution of baseline covariates according to tertiles of
tion, as well as platelet involvement in in vitro coagulation.27,28
                                                                           PDW was calculated using analysis of variance adjusted for age
                                                           ci

Our findings suggest that PDW might be a better marker of
                                                                           (GENMOD procedure for categorical variables and GLM pro-
platelet activation and can therefore be better linked with disease
                                                         er


                                                                           cedure for continuous variables in SAS software, Table 1).
outcomes where platelets are thought to play a role. Based on
                                                                                To estimate the association between PDW tertiles and all-
these findings, we aimed to investigate whether PDW variability
                                                        m



                                                                           cause and cause-specific mortality risk, a multiple imputation
is associated with the risk for all-cause, cardiovascular- and can-
                                                                           approach was used (SAS PROC MI, N=10 imputed datasets;
cer-specific mortality in the Moli-sani study, an adult general
                                              om




                                                                           and PROC MIANALYZE) to maximize data availability. Hazard
population cohort.29,30
                                                                           ratios (HR) and 95% CI for all-cause and cause-specific mortal-
                                                                           ity according to PDW tertiles were calculated using Cox Pro-
                                        -c




                                                                           portional Hazard (unadjusted, age- and sex-adjusted and
Materials and Methods                                                      multivariable) models with time-on-study on the time scale and
                                on




Study population                                                           considering the lowest PDW tertile as reference.
                                                                                The final multivariable models included the variables asso-
     The Moli-sani study is an ongoing prospective cohort study            ciated with both outcome and exposure with a P-value (adjusted
                           N




that enrolled 24,325 individuals (51.9% women, aged ≥35 years)             for age and sex) <0.20.
randomly recruited from the general population of Molise region                 Adjusted survival curves were constructed for all-cause and
in Southern-central Italy between 2005 and 2010.30 The Moli-               cause-specific mortality to show event rates during follow-up
sani study complies with the Declaration of Helsinki and was               by tertiles of PDW.
approved by the Catholic University Ethical Committee, Rome,                    The risks of all-cause and cause-specific mortality for each
Italy. All participants enrolled provided written informed con-            decrease in 1 SD of PDW (0.58 fL) were calculated in the whole
sent. Details of the Moli-sani study have been previously de-              sample.
scribed.30                                                                      Sensitivity analyses were performed: i) considering a case
     For this study, we only included participants recruited in the        complete analysis for all variables (n=15,868 individuals); ii)
center of Campobasso (n=19,211) and excluded recruitments in               excluding early deaths (follow-up time ≥2 years; N=17,198 in-
the center of Termoli (n=5,114) to avoid biases caused using a             dividuals).
different operator and cell counter for the measurement of hema-                Sub-group analyses separately by sex, age classes (35-65
tological parameters. In addition, non-Caucasian participants or           years, ≥65 years), CVD risk factors, and history of CVD or can-
participants with hematological diseases (mainly polycythemia              cer were performed. Multiplicative interaction between PDW
vera, essential thrombocythemia and myelofibrosis), missing                (modeled as tertiles) and the designed effect modifier in relation
measurements of PDW, unreliable medical questionnaire as well              to all-cause and cause-specific mortality was tested with cross-
as individuals with an unknown cause of death were omitted                 product terms.
from the analysis. The final study sample consisted of 17,334                   Data analysis used SAS/STAT software, (Version 9.4 for Win-
individuals [51.6% women; mean age ± standard deviation (SD)               dowsc2009, SAS Institute Inc., Cary, NC, USA).


                                         Bleeding, Thrombosis and Vascular Biology 2023; 2:83
                      Platelet distribution width is associated with cardiovascular mortality in an adult general population                                            21



Table 1. Baseline characteristics according to tertiles of platelet distribution width in the Moli-sani Study cohort (N=17,334).
Characteristic                                                     PDW Tertiles                                              P value
                                          T1                              T2                         T3
N                                                   5145                               6477                                5712
PDW, fL                                          15.8±0.21                           16.3±0.14                          17.0±0.47                                -
 Min-max                                         14.6-16.0                           16.1-16.5                          16.6-20.4                                -
Men                                             1971 (38.3)                         3151 (48.7)                        3273 (57.3)                           <0.0001
Age, years                                       54.8±11.7                           55.5±12.0                         56.6 ± 12.4                           <0.0001
HCT, %                                            43.3±3.9                           43.4±4.0                            43.2±4.2                            0.0084
PLT, x109/L                                     265.8±64.2                          249.5±57.7                         232.8±61.8                            <0.0001
MPV, fL                                          8.52±0.82                           8.53±0.86                          8.72±1.08                            <0.0001
WBC, x109/L                                   6.15 (6.11-6.19)                   6.01 (5.98-6.05)                    5.91 (5.87-5.95)                        <0.0001
Residence                                                                                                                                                      0.45
 Rural                                          1646 (32.0)                         2143 (33.0)                        1887 (33.0)
 Urban                                          3499 (68.0)                         4334 (67.0)                        3825 (67.0)
Education                                                                                                                                                      0.68
 Up to lower secondary school                   2688 (52.3)                         3375 (52.2)                        3082 (54.0)




                                                                                                                 ly
 High school or higher                          2452 (47.7)                         3089 (47.8)                        2634 (46.0)




                                                                                                        on
Income                                                                                                                                                         0.36
 <40000 €/year                                  2614 (50.8)                         3247 (50.1)                        2882 (50.5)
 ≥40000 €/year                                   640 (12.4)                          803 (12.4)                         668 (11.7)
 Not responders                                 1891 (36.8)                         2427 (37.5)                        2162 (37.8)




                                                                                                 e
BMI, kg/m2                                       28.0±4.8                            28.0±4.8                           28.1±4.6                               0.48
 Normal weight
 Overweight
 Obese
                                                1479 (28.8)
                                                2158 (41.9)
                                                1508 (29.3)
                                                                                       us
                                                                                    1819 (28.0)
                                                                                    2723 (42.0)
                                                                                    1933 (30.0)
                                                                                                                       1441 (25.3)
                                                                                                                       2500 (43.8)
                                                                                                                       1765 (30.9)
                                                                                                                                                               0.24
                                                                              al
Waist-to-hip ratio                              0.927±0.079                        0.930±0.077                         0.932±0.074                           0.0046
Mediterranean diet score                           4.4±1.6                            4.4±1.6                             4.3±1.7                             0.035
                                                            ci

Leisure time PA                                                                                                                                                0.18
                                                          er


 Low (≤30min/day)                               2202 (42.8)                         2557 (39.5)                        2192 (38.4)
 High (>30min/day)                              2943 (57.2)                         3920 (60.5)                        3520 (61.6)
                                                         m



Smoking habit                                                                                                                                                 0.067
 Never smoker                                   2640 (51.4)                         3261 (50.4)                        2798 (49.0)
                                             om




 Current smoker                                 1297 (25.2)                         1468 (22.6)                        1185 (20.8)
 Former smoker                                  1202 (23.4)                         1740 (27.0)                        1723 (30.2)
SBP, mm Hg                                      141.3±20.2                          141.7±20.7                         142.4±21.30                           0.0053
                                      -c




DBP, mm Hg                                        82.3±9.5                           82.4±9.6                            82.5±9.9                              0.39
Heart rate, bpm                                  66.8±10.1                           67.2±10.3                          67.1±10.3                              0.16
                             on




Hypertension                                    2878 (56.0)                         3779 (58.4)                        3502 (61.3)                             0.89
Diabetes                                          353 (7.0)                          594 (9.3)                          676 (12.1)                           <0.0001
                       N




Hypercholesterolemia                            1806 (35.6)                         2222 (34.8)                        1882 (33.4)                            0.016
History of CVD                                    220 (4.3)                          319 (5.0)                           340 (6.0)                             0.99
Heart failure                                     28 (0.5)                            42 (0.7)                           45 (0.8)                              0.80
Atrial fibrillation                               55 (1.1)                            86 (1.3)                           112 (2.0)                            0.057
History of cancer                                 157 (3.1)                          196 (3.0)                           187 (3.3)                             0.85
Liver disease                                     169 (3.3)                          240 (3.8)                           242 (4.3)                             0.18
Cystatin C, mg/L                            0.962 (0.957-0.966)                0.962 (0.958-0.966)                 0.967 (0.953-0.972)                         0.14
Creatinine, mg/dL                           0.778 (0.773-0.782)                0.779 (0.775-0.783)                 0.782 (0.778-0.786)                         0.24
hs-CRP, mg/L                                  1.53 (1.48-1.57)                   1.55 (1.52-1.59)                    1.51 (1.47-1.55)                          0.29
d-Dimer, ng/dL                              188.0 (185.9-190.1)                187.4 (185.6-189.3)                 188.3 (186.3-190.3)                         0.82
Fibrinogen, g/L                                  3.09±0.62                           3.08±0.62                          3.02±0.62                            <0.0001
Antiplatelet drugs                                294 (5.7)                          405 (6.3)                           411 (7.2)                             0.49
Only in women, n=8939
 Oral contraceptives                             907 (28.6)                         853 (25.7)                          595 (24.4)                           0.0025
 HRT                                             199 (6.3)                          192 (5.8)                           131 (5.4)                             0.28
Continuous variables are presented as means±standard deviation and categorical variables as number and (percentage). Values for Triglycerides, d-Dimer, C-reactive protein,
Cystatin C, and white blood cells are reported as geometric means with corresponding 95% confidence intervals. Means (except for age) and p values adjusted for age, sex,
and hematocrit.
BMI, body mass index; CVD, cardiovascular disease; DBP, diastolic blood pressure; HCT, hematocrit; HRT, hormone replacement therapy; hs-CRP, high-sensitive C-reactive
protein; MPV, mean platelet volume; PDW, platelet distribution width; PLT, platelet count; SBP, systolic blood pressure; WBC, white blood cell count; PA, physical activity.


                                                 Bleeding, Thrombosis and Vascular Biology 2023; 2:83
22                                                                B. Izzi et al.




                                                                             even when the model was further adjusted for Plt, MPV, and
Results                                                                      WBCs (HR=1.20; 95% CI 1.04-1.37 for all-cause mortality,
                                                                             HR=1.29; 95% CI 1.03-1.62 for cardiovascular mortality and
     In the Moli-sani population, the average of PDW was                     HR=1.27; 95% CI 0.97-1.68 for other-cause mortality; Figure 2
16.4±0.58 fL, and its distribution displayed features of normality           and Supplementary Table 1, Model 3). No association was iden-
(Supplementary Figure 2).                                                    tified between PDW and cancer mortality (Figure 2 and Supple-
     Table 1 reports baseline characteristics of the Moli-sani cohort        mentary Table 1).
according to tertiles of PDW. Subjects in the highest PDW tertile                 Analyses performed in a sub-sample (n=14,335), for which
(16.6-20.4 fL) were more frequently men and older as compared                baseline fibrinogen measurements were available, showed that the
to those in the first tertile (14.6-16.0 fL). They had significantly         associations were independent of this inflammatory and hemosta-
lower Plt, hematocrit, white blood cell counts and fibrinogen lev-           tic factor (Supplementary Table 2, Model 4).
els and higher MPV. Individuals with higher PDW also had a                        Case-complete analyses restricted to 15,868 subjects without
higher waist-to-hip ratio and systolic blood pressure. They re-              missing values for covariates and analyses of data after exclusion
ported a greater prevalence of diabetes and atrial fibrillation and          of early deaths (follow-up≥2 years, N=17,198 individuals),
had a lower prevalence of hypercholesterolemia and adherence to              yielded very similar results (Supplementary Tables 3-4).
the Mediterranean diet. In addition, women in the highest PDW                     Sensitivity subgroup analyses were reported in Figure 3 and
tertile were less frequently under oral contraceptive treatment.             Supplementary Tables 5-8.
     Over a median follow-up of 11.6 years (interquartile range                   No difference in the association between PDW tertiles and




                                                                                                        ly
10.7 to 12.5 years; 195,521.4 person-years) 1,535 all-cause deaths           all-cause and specific-cause mortality according to sex, diabetes,




                                                                                                on
were validated (579 cardiovascular and 561 cancer deaths).                   hypercholesterolemia, smoking habits, history of CVD, and can-
     Multivariable survival curves for all-cause mortality accord-           cer was found (Supplementary Tables 5-8, P for interaction >0.05).
ing to PDW tertiles were well separated (Figure 1, P=0.019). In a                 However, subgroup analyses separately by age classes (35-




                                                                                          e
model adjusted for age, sex, hematocrit, cystatin C, systolic BP,            65 years, ≥65 years) showed a risk trend with CVD mortality in
atrial fibrillation, liver disease, hypercholesterolemia, diabetes,          both younger and older individuals while the association with all-
waist-to-hip ratio, Mediterranean diet score, physical
activity/leisure time and smoking, compared to individuals within
                                                                                   us
                                                                             cause mortality was only confirmed in the elderly (HR: 1.34; 1.14-
                                                                             1.58, p for interaction =0.028, Figure 3 and Supplementary Table
the lowest PDW tertile (14.6-16.0 fL), subjects in the highest               5). Similar results were found in the elderly for cancer mortality
                                                                           al
PDW tertile (16.2-20.4 fL) had an increased risk of all-cause                (age ≥65 years: HR: 1.37; 1.01-1.85, P for interaction =0.020; Fig-
                                                             ci

(HR=1.21; 95% CI 1.06-1.39 for highest vs. lowest tertile of                 ure 3 and Supplementary Table 7). In contrast, we observed an in-
PDW; Supplementary Table 1, Model 2), cardiovascular                         verse association between PDW and cancer-specific mortality in
                                                           er


(HR=1.25; 95% CI 1.00-1.55; Supplementary Table 1, Model 2)                  the subgroup 35-65 years compared to the one observed in the
and other-cause mortality (HR=1.40; 95% CI 1.07-1.86; Supple-                elderly (HR: 0.77; 0.55-1.08, P for interaction =0.020; Figure 3
                                                          m



mentary Table 1, Model 2). These findings remained consistent                and Supplementary Table 7).
                                               om
                                         -c
                                 on
                            N




Figure 1. Multivariable survival estimates for all-cause mortality according to platelet distribution width tertiles. Multivariable survival
curves were obtained from the multivariable model adjusted for age, sex, hematocrit; systolic blood pressure, atrial fibrillation, liver
disease, hypercholesterolemia, Cystatin C, Mediterranean diet score, physical activity/leisure time, smoking, diabetes, waist-to-hip
ratio, platelet count, mean platelet volume, white blood cell count.


                                          Bleeding, Thrombosis and Vascular Biology 2023; 2:83
                  Platelet distribution width is associated with cardiovascular mortality in an adult general population                    23




                                                                                               ly
                                                                                       on
                                                                                 e
                                                                         us
                                                                  al
Figure 2. Hazard ratios (95% confidence intervals) for all-cause (overall), cardiovascular and cancer mortality, according to platelet
distribution width tertiles. Multivariable model adjusted for age, sex, hematocrit; systolic blood pressure, atrial fibrillation, liver disease,
                                                   ci

hypercholesterolemia, Cystatin C, Mediterranean diet score, physical activity/leisure time, smoking, diabetes, waist-to-hip ratio, platelet
count, mean platelet volume, white blood cell count.
                                                 er
                                                m
                                      om
                                -c
                        on
                   N




Figure 3. Hazard ratios (95% confidence intervals) of subgroup analysis by age for all-cause, cardiovascular, cancer and other-cause
mortality, according to platelet distribution width tertiles. Multivariable model adjusted for age, sex, hematocrit; systolic blood pressure,
atrial fibrillation, liver disease, hypercholesterolemia, Cystatin C, Mediterranean diet score, physical activity/leisure time, smoking, di-
abetes, waist-to-hip ratio, platelet count, mean platelet volume, white blood cell count. Multiplicative interaction between platelet dis-
tribution width (modelled as tertiles) and age as the effect modifier in relation to all-cause and cause specific mortality was tested with
cross-product terms.


                                          Bleeding, Thrombosis and Vascular Biology 2023; 2:83
24                                                               B. Izzi et al.




                                                                            ber) has long been considered to be one of the most relevant
Discussion                                                                  markers.44-54 PDW is a marker reflecting the heterogeneity of
                                                                            the platelet population size. Overall, all the possible processes
     In a general Italian adult population, PDW was associated              affecting platelet volume and morphology might be responsible
with cardiovascular mortality as also observed when dividing                for PDW variability, including platelet activation, production,
the cohort into age-based subgroups. Further age-dependent                  and destruction.16,55-57
specific relationships between PDW and all-cause and cause-                      On one hand, our findings of PDW being associated with
specific mortality were identified (Figure 4).                              an increased risk for cardiovascular mortality might be sugges-
     To the best of our knowledge, this study is the first to report        tive of progressive platelet activation in individuals with higher
data on CVD mortality risk associated with PDW in a general                 PDW, with subsequent changes in the morphology of platelets
population. Previous studies have largely reported the associa-             and pseudopodia formation.16,55 We and others have observed
tion of PDW with clinical correlates as well as disease progno-             that PDW variability is linked to platelet function/activation
sis in specific pathological conditions including CVD, cancer,              measured cross-sectionally both in vitro and ex vivo in thrombo-
metabolic syndrome, diabetes, and neurological disorders,21-26              inflammation and coagulation-dependent assays, specifically,
but also including pediatric and neonatal sepsis,31,32 and preterm          even better than MPV.16,27 Even if the association identified be-
infant mortality.33                                                         tween PDW and platelet activation observed cross-sectionally
     In line with our main findings, higher PDW has been previ-             in the Moli-family cohort had an inverse sign compared to the
ously identified as a good prognostic marker for hospital-related           association we observed for cardiovascular-specific mortality




                                                                                                      ly
mortality in the context of heart failure,34 acute pulmonary throm-         in the Moli-sani study, in both cases, PDW appeared to explain




                                                                                               on
boembolism,35-38 coronary syndrome,39,40 myocardial infarc-                 these associations better than MPV variability.9,27
tion,41,42 and deep venous thrombosis.43 In addition, in the study               On the other hand, greater platelet heterogeneity might also
by Tzur et al.,12 that showed an increased risk of 90-day mortality         be the result of an increased hypercoagulability after the initi-
and shortened survival after discharge from the hospital for pa-




                                                                                         e
                                                                            ation of thrombotic events, which would stimulate throm-
tients with high PDW values, it was also reported that subjects             bopoiesis alongside increased platelet consumption/
with PDW >16.7% on hospital admission were more likely to be
admitted for cardio-cerebrovascular disorders and to present with
                                                                                  us
                                                                            destruction.56,57 The latter is supported by the evidence in our
                                                                            cohort that individuals within the third PDW tertile also showed
several concomitant CVD-related comorbidities.12                            higher MPV and lower Plt (Table 1). In addition, increased
                                                                          al
     Among the major determinants of platelet influence in he-              platelet consumption/destruction might also depend upon an al-
                                                            ci

mostasis, thrombosis, and inflammation, platelet size (and num-             tered bone marrow activity of which the acquisition of somatic
                                                          er
                                                         m
                                               om
                                         -c
                                 on
                            N




Figure 4. Graphical abstract.


                                          Bleeding, Thrombosis and Vascular Biology 2023; 2:83
                 Platelet distribution width is associated with cardiovascular mortality in an adult general population                      25



mutations causing clonal expansion advantage [clonal
hematopoiesis of indeterminate potential (CHIP)] is one of the          References
causes and an emerging risk factor for CVD58-60 and all-cause
                                                                         1. Kabat GC, Kim MY, Verma AK, et al. Platelet count and total
mortality.61 Indeed, a recent large study conducted in 200,453
                                                                            and cause-specific mortality in the Women's Health Initiative.
UK Biobank participants showed highly significant associations
                                                                            Ann Epidemiol 2017;27:274-80.
between the presence of clonal hematopoiesis of indeterminate
                                                                         2. Msaouel P, Lam AP, Gundabolu K, et al. Abnormal platelet
potential (CHIP) mutations and PDW, the most significantly as-
                                                                            count is an independent predictor of mortality in the elderly
sociated blood trait among others including Plt and Pct.62
                                                                            and is influenced by ethnicity. Haematologica 2014;99:
    As a surrogate of platelet production/turnover, PDW might
                                                                            930-6.
also indirectly reflect the presence of reticulated or larger            3. Tsai MT, Chen YT, Lin CH, et al. U-shaped mortality curve
platelets. A number of preclinical studies as well as studies con-          associated with platelet count among older people: a commu-
ducted in healthy donors point to striking biological differences           nity-based cohort study. Blood 2015;126:1633-5.
in both the transcriptome and the proteome of larger platelets           4. Bonaccio M, Di Castelnuovo A, Costanzo S, et al. Age- and
or RNA-rich reticulated platelets.51,63 The latter, in particular,          sex-based ranges of platelet count and cause-specific mortal-
represents a specific platelet subpopulation with a higher throm-           ity risk in an adult general population: prospective findings
bogenic potential and has found to be significantly elevated in             from the Moli-sani study. Platelets 2018;29:312-5.
patients with coronary artery disease and acute coronary syn-            5. Bonaccio M, Di Castelnuovo A, Costanzo S, et al. Age-sex-




                                                                                            ly
drome and have been associated with adverse cardiovascular                  specific ranges of platelet count and all-cause mortality:
events.64-68                                                                prospective findings from the MOLI-SANI study. Blood




                                                                                   on
    Finally, we also observed a significantly increased cancer-             2016;127:1614-6.
specific mortality by PDW in the elderly (age ≥65 years, Figure          6. Vinholt PJ, Hvas AM, Frederiksen H, et al. Platelet count is
3) showing an opposite direction of the one observed in the                 associated with cardiovascular disease, cancer and mortality:




                                                                             e
younger subjects. This association appeared to drive most of                A population-based cohort study. Thromb Res 2016;148:
the association identified with all-cause mortality, which, in
fact, was only confirmed in the elderly. Despite PDW has been
shown to be a good cancer prognosis biomarker, also as predic-
                                                                     us     136-42.
                                                                         7. Thaulow E, Erikssen J, Sandvik L, et al. Blood platelet count
                                                                            and function are related to total and cardiovascular death in
                                                                al
tor of mortality in cancer patients,21 data on PDW variability in           apparently healthy men. Circulation 1991;84:613-7.
relation to cancer mortality in elderly cohorts of the general           8. van der Bom JG, Heckbert SR, Lumley T, et al. Platelet count
                                                  ci

population are not available. Further studies on elderly cohorts            and the risk for thrombosis and death in the elderly. J Thromb
                                                er


with adequate numbers of incident cancer events should be con-              Haemost 2009;7:399-405.
ducted to substantiate these findings.                                   9. Bonaccio M, Di Castelnuovo A, Costanzo S, et al. Mean
                                               m



                                                                            platelet volume is associated with lower risk of overall and
Strengths and limitations                                                   non-vascular mortality in a general population. Results from
                                     om




                                                                            the Moli-sani study. Thromb Haemost 2017;117:1129-40.
     The major strengths of this study are its prospective design       10. Panova-Noeva M, Schulz A, Hermanns MI, et al. Sex-specific
on a large general adult population and the availability of a wide          differences in genetic and nongenetic determinants of mean
number of lifestyle, anthropometric, and biological variables,
                               -c




                                                                            platelet volume: results from the Gutenberg Health Study.
allowing for a better understanding of the predictive role of               Blood 2016;127:251-9.
                        on




PDW levels controlling for their possible confounding effects.          11. Slavka G, Perkmann T, Haslacher H, et al. Mean platelet vol-
A limitation is the lack of longitudinal PDW measurements,                  ume may represent a predictive parameter for overall vascular
which makes it unlikely to fully capture exposure over the life             mortality and ischemic heart disease. Arterioscler Thromb
                  N




course. Additionally, PDW measures are partially dependent on               Vasc Biol 2011;31:1215-8.
the technology used to record them.69 However, all PDW meas-            12. Tzur I, Barchel D, Izhakian S, et al. Platelet distribution width:
urements were performed using the same instrument and by the                a novel prognostic marker in an internal medicine ward. J
same operators, virtually excluding all pre-analytical and ana-             Community Hosp Intern Med Perspect 2019;9:464-70.
lytical variables. Finally, the findings here reported derive from      13. Sachdev R, Tiwari AK, Goel S, et al. Establishing biological
an observational study, thus causality cannot be inferred.                  reference intervals for novel platelet parameters (immature
                                                                            platelet fraction, high immature platelet fraction, platelet dis-
                                                                            tribution width, platelet large cell ratio, platelet-X, plateletcrit,
Conclusions                                                                 and platelet distribution width) and their correlations among
                                                                            each other. Indian J Pathol Microbiol 2014;57:231-5.
     Up to now, PDW had never been investigated as a potential          14. Budak YU, Polat M, Huysal K. The use of platelet indices,
predictor of mortality risk in general populations. The PDW-                plateletcrit, mean platelet volume and platelet distribution
associated increase in CVD mortality risk reported in this study            width in emergency non-traumatic abdominal surgery: a sys-
could be related to accelerated/altered platelet activation, pro-           tematic review. Biochem Med (Zagreb) 2016;26:178-93.
duction, or destruction, all age-dependent processes leading to         15. Wiwanitkit V. Plateletcrit, mean platelet volume, platelet dis-
CVD and CVD-related comorbidities and death. Further stud-                  tribution width: its expected values and correlation with par-
ies in general populations should be undertaken to better clar-             allel red blood cell parameters. Clin Appl Thromb Hemost
ify the PDW involvement in all-cause and cancer mortality in                2004;10:175-8.
the elderly.                                                            16. Vagdatli E, Gounari E, Lazaridou E, et al. Platelet distribution


                                        Bleeding, Thrombosis and Vascular Biology 2023; 2:83
26                                                                   B. Izzi et al.




      width: a simple, practical and specific marker of activation of                 Diagnostic Biomarker Tests of Neonatal Sepsis: A Prospective
      coagulation. Hippokratia 2010;14:28-32.                                         Study from a Tertiary Care Hospital in a Low-Income Coun-
17.   Borkataky S, Jain R, Gupta R, et al. Role of platelet volume                    try. Biomed Res Int 2022;2022:5166380.
      indices in the differential diagnosis of thrombocytopenia: a              33.   Hsieh PY, Hsu KH, Chiang MC, et al. Platelet parameters and
      simple and inexpensive method. Hematology 2009;                                 the association with morbidity and mortality in Preterm In-
      14:182-6.                                                                       fants. Pediatr Neonatol 2023;64:68-74.
18.   Lee E, Kim M, Jeon K, et al. Mean Platelet Volume and                     34.   Sato Y, Yoshihisa A, Watanabe K, et al. Association between
      Platelet Distribution Width Indicate that Platelets Remain                      platelet distribution width and prognosis in patients with heart
      Small for Most of Their Lifespans in Patients with Essential                    failure. PLoS One 2020;15:e0244608.
      Thrombocythemia. Clin Lab 2019;65.                                        35.   Ghaffari S, Parvizian N, Pourafkari L, et al. Prognostic value
19.   Luzzatto G, de Franchis G, Fabris F, et al. Increased propor-                   of platelet indices in patients with acute pulmonary throm-
      tion of giant platelets and platelet distribution width are better              boembolism. J Cardiovasc Thorac Res 2020;12:56-62.
      indicators of altered platelet homeostasis than mean platelet             36.   Araz O, Albez FS, Ucar EY, et al. Predictive Value of Mean
      volume in liver cirrhosis. Folia Haematol Int Mag Klin Mor-                     Platelet Volume for Pulmonary Embolism Recurrence. Lung
      phol Blutforsch 1988;115:719-26.                                                2017;195:497-502.
20.   Ntaios G, Papadopoulos A, Chatzinikolaou A, et al. Increased              37.   Zeng W, Xu B, Wang X, et al. Identification of Prognostic
      values of mean platelet volume and platelet size deviation                      Factors for Recurrence and Mortality in Patients With Acute
      width may provide a safe positive diagnosis of idiopathic                       Pulmonary Embolism. Heart Surg Forum 2022;25:




                                                                                                             ly
      thrombocytopenic purpura. Acta Haematol 2008;119:173-7.                         E812-E821.




                                                                                                     on
21.   Xia W, Chen W, Tu J, et al. Prognostic Value and Clinico-                 38.   Wikan VE, Tondel BG, Morelli VM, et al. Diagnostic Blood
      pathologic Features of Platelet Distribution Width in Cancer:                   Biomarkers for Acute Pulmonary Embolism: A Systematic
      A Meta-Analysis. Med Sci Monit 2018;24:7130-6.                                  Review. Diagnostics (Basel) 2023;13.




                                                                                               e
22.   Weymann A, Ali-Hasan-Al-Saegh S, Sabashnikov A, et al.                    39.   Kowara M, Grodecki K, Huczek Z, et al. Platelet distribution
      Platelets Cellular and Functional Characteristics in Patients
      with Atrial Fibrillation: A Comprehensive Meta-Analysis
      and Systematic Review. Med Sci Monit Basic Res 2017;23:
                                                                                       us
                                                                                      width predicts left ventricular dysfunction in patients with
                                                                                      acute coronary syndromes treated with percutaneous coronary
                                                                                      intervention. Kardiol Pol 2017;75:42-7.
                                                                              al
      58-86.                                                                    40.   Timoteo AT, Papoila AL, Lousinha A, et al. Predictive impact
23.   Budzianowski J, Pieszko K, Burchardt P, et al. The Role of                      on medium-term mortality of hematological parameters in
                                                               ci

      Hematological Indices in Patients with Acute Coronary Syn-                      Acute Coronary Syndromes: added value on top of GRACE
      drome. Dis Markers 2017;2017:3041565.                                           risk score. Eur Heart J Acute Cardiovasc Care 2015;4:172-9.
                                                             er


24.   Zaccardi F, Rocca B, Pitocco D, et al. Platelet mean volume,              41.   Bae MH, Lee JH, Yang DH, et al. White blood cell, hemo-
      distribution width, and count in type 2 diabetes, impaired fast-                globin and platelet distribution width as short-term prognostic
                                                            m




      ing glucose, and metabolic syndrome: a meta-analysis. Dia-                      markers in patients with acute myocardial infarction. J Korean
                                                  om




      betes Metab Res Rev 2015;31:402-10.                                             Med Sci 2014;29:519-26.
25.   Izzi B, Tirozzi A, Cerletti C, et al. Beyond Haemostasis and              42.   Rechcinski T, Jasinska A, Forys J, et al. Prognostic value of
      Thrombosis: Platelets in Depression and Its Co-Morbidities.                     platelet indices after acute myocardial infarction treated with
      Int J Mol Sci 2020;21.                                                          primary percutaneous coronary intervention. Cardiol J
                                            -c




26.   Izzi B, Fuster JJ. Blood platelet heterogeneity at a glance:                    2013;20:491-8.
                                    on




      new insights into the relationship between platelet distribu-             43.   Oguz S. Relationship between First Values of Red Cell Dis-
      tion width and neuropsychiatric disorders. Bleed Thromb                         tribution Width, Mean Platelet Volume, Platelet Distribution
      Vasc Biol 2023;2.                                                               Width, and Hospital Mortality in Acute Deep Venous Throm-
                              N




27.   Izzi B, Gialluisi A, Gianfagna F, et al. Platelet Distribution                  bosis. J Coll Physicians Surg Pak 2021;30:379-82.
      Width Is Associated with P-Selectin Dependent Platelet                    44.   Mangalpally KK, Siqueiros-Garcia A, Vaduganathan M, et al.
      Function: Results from the Moli-Family Cohort Study. Cells                      Platelet activation patterns in platelet size sub-populations:
      2021;10.                                                                        differential responses to aspirin in vitro. J Thromb Thrombol-
28.   Izzi B, Gianfagna F, Yang WY, et al. Variation of PEAR1                         ysis 2010;30:251-62.
      DNA methylation influences platelet and leukocyte function.               45.   Mezzano D, Aranda E, Foradori A. Comparative study of size,
      Clin Epigenetics 2019;11:151.                                                   total protein, fibrinogen and 5-HT content of human and ca-
29.   Di Castelnuovo A, de Curtis A, Costanzo S, et al. Association                   nine platelet density subpopulations. Thromb Haemost
      of D-dimer levels with all-cause mortality in a healthy adult                   1986;56:288-92.
      population: findings from the MOLI-SANI study. Haemato-                   46.   Polanowska-Grabowska R, Raha S, Gear AR. Adhesion ef-
      logica 2013;98:1476-80.                                                         ficiency, platelet density and size. Br J Haematol 1992;82:
30.   Iacoviello L, Bonanni A, Costanzo S, et al. The MOLI-SANI                       715-20.
      Project, a randomized, prospective cohort study in the Molise             47.   Frojmovic M, Wong T. Dynamic measurements of the platelet
      region in Italy; design, rationale and objectives. Ital J Pub                   membrane glycoprotein IIb-IIIa receptor for fibrinogen by
      Health 2007:110-8.                                                              flow cytometry. II. Platelet size-dependent subpopulations.
31.   Celegen M, Kesici S, Yavuz S, et al. Are platelet indices                       Biophys J 1991;59:828-37.
      promising ratios for predicting pediatric septic shock progno-            48.   Handtke S, Steil L, Palankar R, et al. Role of Platelet Size Re-
      sis? Bratisl Lek Listy 2022;123:444-8.                                          visited-Function and Protein Composition of Large and Small
32.   Khadka P, Maharjan G, Chapagain G, et al. Economic and                          Platelets. Thromb Haemost 2019;119:407-20.


                                             Bleeding, Thrombosis and Vascular Biology 2023; 2:83
                    Platelet distribution width is associated with cardiovascular mortality in an adult general population                                    27



49. Karpatkin S. Heterogeneity of human platelets. I. Metabolic                            and Risk of Atherosclerotic Cardiovascular Disease. N Engl
    and kinetic evidence suggestive of young and old platelets. J                          J Med 2017;377:111-21.
    Clin Invest 1969;48:1073-82.                                                     60.   Haybar H, Shahrabi S, Ghanavat M, Khodadi E. Clonal
50. Chamberlain KG, Penington DG. Monoamine oxidase and                                    hematopoiesis: Genes and underlying mechanisms in car-
    other mitochondrial enzymes in density subpopulations of                               diovascular disease development. J Cell Physiol 2019;234:
    human platelets. Thromb Haemost 1988;59:29-33.                                         8396-401.
51. Clancy L, Beaulieu LM, Tanriverdi K, Freedman JE. The role                       61.   Jaiswal S, Fontanillas P, Flannick J, et al. Age-related clonal
    of RNA uptake in platelet heterogeneity. Thromb Haemost                                hematopoiesis associated with adverse outcomes. N Engl J
    2017;117:948-61.                                                                       Med 2014;371:2488-98.
52. Thompson CB, Jakubowski JA, Quinn PG, et al. Platelet size                       62.   Kar SP, Quiros PM, Gu M, et al. Genome-wide analyses of
    as a determinant of platelet function. J Lab Clin Med                                  200,453 individuals yield new insights into the causes and
    1983;101:205-13.                                                                       consequences of clonal hematopoiesis. Nat Genet 2022;54:
53. Li BY, He SZ, Li WH. Heterogeneity of human platelet den-                              1155-66.
    sity subpopulations in aggregation, secretion of ATP, and cy-                    63.   Allan HE, Hayman MA, Marcone S, et al. Proteome and func-
    tosolic-free calcium concentration. Zhongguo Yao Li Xue Bao                            tional decline as platelets age in the circulation. J Thromb
    1996;17:152-5.                                                                         Haemost 2021;19:3095-112.
54. Selvadurai MV, Hamilton JR. Structure and function of the                        64.   Bongiovanni D, Santamaria G, Klug M, et al. Transcriptome
    open canalicular system - the platelet's specialized internal                          Analysis of Reticulated Platelets Reveals a Prothrombotic




                                                                                                           ly
    membrane network. Platelets 2018;29:319-25.                                            Profile. Thromb Haemost 2019;119:1795-806.




                                                                                                  on
55. Kamisli O, Kamisli S, Kablan Y, et al. The prognostic value                      65.   Cesari F, Marcucci R, Gori AM, et al. Reticulated platelets
    of an increased mean platelet volume and platelet distribution                         predict cardiovascular death in acute coronary syndrome pa-
    width in the early phase of cerebral venous sinus thrombosis.                          tients. Insights from the AMI-Florence 2 Study. Thromb




                                                                                           e
    Clin Appl Thromb Hemost 2013;19:29-32.                                                 Haemost 2013;109:846-53.
56. Sevuk U, Bahadir MV, Altindag R, et al. Value of serial
    platelet indices measurements for the prediction of pulmonary
    embolism in patients with deep venous thrombosis. Ther Clin
                                                                                  us 66.   Grove EL, Hvas AM, Mortensen SB, et al. Effect of platelet
                                                                                           turnover on whole blood platelet aggregation in patients with
                                                                                           coronary artery disease. J Thromb Haemost 2011;9:185-91.
                                                                          al
    Risk Manag 2015;11:1243-9.                                                       67.   Ibrahim H, Schutt RC, Hannawi B, et al. Association of im-
57. Ulucan S, Keser A, Kaya Z, et al. Association between PDW                              mature platelets with adverse cardiovascular outcomes. J Am
                                                         ci

    and Long Term Major Adverse Cardiac Events in Patients                                 Coll Cardiol 2014;64:2122-9.
    with Acute Coronary Syndrome. Heart Lung Circ                                    68.   Anetsberger A, Blobner M, Haller B, et al. Immature platelets
                                                       er


    2016;25:29-34.                                                                         as a novel biomarker for adverse cardiovascular events in pa-
58. Jaiswal S, Libby P. Clonal haematopoiesis: connecting ageing                           tients after non-cardiac surgery. Thromb Haemost 2017;117:
                                                      m




    and inflammation in cardiovascular disease. Nat Rev Cardiol                            1887-95.
                                           om




    2020;17:137-44.                                                                  69.   Briggs C. Quality counts: new parameters in blood cell count-
59. Jaiswal S, Natarajan P, Silver AJ, et al. Clonal Hematopoiesis                         ing. Int J Lab Hematol 2009;31:277-97.
                                    -c
                           on




Online supplementary material:
Supplementary methods: “Blood and biochemical parameters” and “Covariate assessment”.
Supplementary Figure S1. Flow chart of selection of the studied population among Moli-sani participants. The groups of eliminated participants (out of the
                     N




    24,325 recruited at baseline) are overlaid. The final study sample cannot be calculated as a subtraction of the sum of eliminated groups out of the
    recruited subjects at baseline.
Supplementary Figure S2. Platelet distribution width (fL) distribution in the Moli-sani cohort.
Supplementary Figure S3. Multivariable survival estimates for cardiovascular disease (panel A, P=0.058), cancer (panel B, P=0.73) and other-cause (panel
    C, P=0.19) mortality according to platelet distribution width tertiles. Multivariable survival curves were obtained from the multivariable model adjusted
    for age, sex, hematocrit; systolic blood pressure, atrial fibrillation, liver disease, hypercholesterolemia, Cystatin C, Mediterranean diet score, physical
    activity/leisure time, smoking, diabetes, waist-to-hip ratio, platelet count, mean platelet volume, white blood cell count by using the first imputed dataset.
    The other imputed datasets are similar and thus omitted.
Supplementary Table S1. Hazard ratios (95% confidence intervals) for all-cause, cardiovascular, cancer and other-cause mortality, according to platelet
    distribution width tertiles (17,334).
Supplementary Table S2. Hazard ratios (95% confidence intervals) for all-cause, cardiovascular and cancer mortality, according to platelet distribution
    width tertiles excluding subjects with missing value for plasmatic fibrinogen (n=14,335).
Supplementary Table S3. Sensitivity analyses for the association of platelet distribution width with all-cause and cause specific mortality considering a case
    complete approach (n=15,868).
Supplementary Table S4. Sensitivity analyses for the association of platelet distribution width with all-cause mortality excluding early deaths (follow-up >2
    years, n=17,198).
Supplementary Table S5. Subgroup analyses for the association of platelet distribution width with all-cause mortality.
Supplementary Table S6. Subgroup analyses for the association of platelet distribution width with cardiovascular disease mortality.
Supplementary Table S7. Subgroup analyses for the association of platelet distribution width with cancer mortality.
Supplementary Table S8. Subgroup analyses for the association of platelet distribution width with other-cause mortality.


                                               Bleeding, Thrombosis and Vascular Biology 2023; 2:83
28                                                           B. Izzi et al.




Appendix: Moli-sani investigators
The enrolment phase of the Moli-sani study was conducted at the Research Laboratories of the Catholic University in Campobasso (Italy),
the follow-up of the Moli-sani cohort is being conducted at the Department of Epidemiology and Prevention of the IRCCS Neuromed,
Pozzilli, Italy.

Steering committee: Licia Iacoviello*° (Chairperson), Giovanni de Gaetano* and Maria Benedetta Donati*.
Scientific secretariat: Marialaura Bonaccio*, Americo Bonanni*, Chiara Cerletti*, Simona Costanzo*, Amalia De Curtis*,
   Augusto Di Castelnuovo§, Alessandro Gialluisi*°, Francesco Gianfagna°§, Mariarosaria Persichillo*, Teresa Di Prospero*
   (Secretary).
Safety and ethical committee: Jos Vermylen (Catholic University, Leuven, Belgio) (Chairperson), Renzo Pegoraro (Pontificia
   Accademia per la Vita, Roma, Italy), Antonio Spagnolo (Catholic University, Roma, Italy).
External event adjudicating committee: Deodato Assanelli (Brescia, Italy), Livia Rago (Campobasso, Italy).
Baseline and follow-up data management: Simona Costanzo* (Coordinator), Marco Olivieri (Campobasso, Italy), Teresa
   Panzera*.
Data analysis: Augusto Di Castelnuovo§ (Coordinator), Marialaura Bonaccio*, Simona Costanzo*, Simona Esposito*,




                                                                                                 ly
   Alessandro Gialluisi*°, Francesco Gianfagna°§, Sabatino Orlandi*, Emilia Ruggiero*, Alfonsina Tirozzi*.




                                                                                         on
Biobank, molecular and genetic laboratory: Amalia De Curtis* (Coordinator), Sara Magnacca§, Fabrizia Noro*, Alfonsina
   Tirozzi*.
Recruitment staff: Mariarosaria Persichillo* (Coordinator), Francesca Bracone*, Teresa Panzera*.




                                                                                    e
Communication and press office: Americo Bonanni*.
                                                                              us
Regional institutions: Direzione Generale per la Salute - Regione Molise; Azienda Sanitaria Regionale del Molise (ASReM,
   Italy); Agenzia Regionale per la Protezione Ambientale del Molise (ARPA Molise, Italy); Molise Dati Spa (Campobasso,
   Italy); Offices of vital statistics of the Molise region.
                                                                      al
Hospitals: Presidi Ospedalieri ASReM: Ospedale A. Cardarelli, Campobasso; Ospedale F. Veneziale, Isernia; Ospedale San
   Timoteo, Termoli (CB); Ospedale Ss. Rosario, Venafro (IS); Ospedale Vietri, Larino (CB); Ospedale San Francesco Carac-
                                                        ci

   ciolo, Agnone (IS); Casa di Cura Villa Maria, Campobasso; Ospedale Gemelli Molise, Campobasso; IRCCS Neuromed,
                                                      er


   Pozzilli (IS), Italy.
                                                     m




*Department of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
°Department of Medicine and Surgery, University of Insubria, Varese, Italy.
                                            om




§
 Mediterranea Cardiocentro, Napoli, Italy.

Moli-sani Study Past Investigators are available at https://www.moli-sani.org/?page_id=173
                                      -c
                               on
                          N




                                       Bleeding, Thrombosis and Vascular Biology 2023; 2:83
