Luigi Ferrucci

California Pacific Medical Center Research Institute, San Francisco, California, United States

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Publications (881)5562.81 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Short sleep duration has been associated with greater risks of obesity, hypertension, diabetes, and cardiovascular disease. Also, common genetic variants in the human Circadian Locomotor Output Cycles Kaput (CLOCK) show associations with ghrelin and total energy intake. We examined associations between habitual sleep duration, body mass index (BMI), and macronutrient intake and assessed whether CLOCK variants modify these associations. We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations of sleep duration and BMI and macronutrient intake as percentages of total energy as well as interactions with CLOCK variants from 9 cohort studies including up to 14,906 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. We observed a significant association between sleep duration and lower BMI (β ± SE = 0.16 ± 0.04, P < 0.0001) in the overall sample; however, associations between sleep duration and relative macronutrient intake were evident in age- and sex-stratified analyses only. We observed a significant association between sleep duration and lower saturated fatty acid intake in younger (aged 20-64 y) adults (men: 0.11 ± 0.06%, P = 0.03; women: 0.10 ± 0.05%, P = 0.04) and with lower carbohydrate (-0.31 ± 0.12%, P < 0.01), higher total fat (0.18 ± 0.09%, P = 0.05), and higher PUFA (0.05 ± 0.02%, P = 0.02) intakes in older (aged 65-80 y) women. In addition, the following 2 nominally significant interactions were observed: between sleep duration and rs12649507 on PUFA intake and between sleep duration and rs6858749 on protein intake. Our results indicate that longer habitual sleep duration is associated with lower BMI and age- and sex-specific favorable dietary behaviors. Differences in the relative intake of specific macronutrients associated with short sleep duration could, at least in part, explain previously reported associations between short sleep duration and chronic metabolic abnormalities. In addition, the influence of obesity-associated CLOCK variants on the association between sleep duration and macronutrient intake suggests that longer habitual sleep duration could ameliorate the genetic predisposition to obesity via a favorable dietary profile. Trials related to this study were registered at clinicaltrials.gov as NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT01331512 [Invecchiare in Chianti (Aging in the Chianti Area) study], NCT00289237 (Inter99), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis). © 2015 American Society for Nutrition.
    American Journal of Clinical Nutrition 01/2015; 101(1):135-43. · 6.50 Impact Factor
  • The Journal of Nutrition Health and Aging 12/2014; in press. · 2.39 Impact Factor
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    ABSTRACT: Background: US studies suggest that leptin, a fat-derived hormone, may be protective against the development of dementia. Objective: To investigate the complex relationship between leptin levels and cognitive decline in elderly Italians. Methods: We studied circulating fasting leptin levels in 809 elderly adults free from dementia who participated in the prospective Italian population-based InCHIANTI study between 1998 and 2009 (mean follow-up of 8.0 years). Global cognitive decline was defined as a reduction of ≥5 points on the Mini-Mental State Examination (MMSE). Trail-Making Tests A and B were also incorporated, with cognitive decline defined as discontinued testing or the worst 10% of change from baseline. We also investigated whether any association could be explained by midlife weight and whether cognitive decline was associated with changing leptin levels. Results: The multivariate adjusted relative risk ([RR]; 95% confidence interval [CI]) of cognitive decline on the MMSE was 0.84 (95% CI 0.73-0.97) in relation to baseline sex-standardized log-leptin levels. High leptin levels showed a non-significant trend toward a reduced risk of decline on the Trail-Making Tests A (RR = 0.85, 95% CI 0.71-1.02) and B (RR = 0.90, 0.79-1.02). Adjusting for midlife weight or change in weight did not alter the pattern of results, and cognitive decline was not associated with changing leptin levels. Conclusions: High leptin levels were independently associated with a reduced risk of cognitive decline in elderly Italians.
    Journal of Alzheimer's disease: JAD 12/2014; · 4.17 Impact Factor
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    ABSTRACT: The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
    Nature Genetics 12/2014; · 35.21 Impact Factor
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    ABSTRACT: Faster resting heart rate (HR), which is associated with inflammation and elevated cortisol levels, is a risk factor for excess cardiovascular morbidity and mortality. Obesity is associated with increased cardiovascular morbidity and mortality, inflammation, and elevated cortisol levels. The aim of the present study was to evaluate the interaction of Body Mass Index (BMI) with inflammation and cortisol in modulating HR in older subjects. We analyzed data of 895 participants aged 65+ enrolled in the "InCHIANTI" study, in sinus rhythm, and not taking beta blockers or digoxin. Linear regression was performed to assess the adjusted association between HR, IL-6, and cortisol levels. The model was also analyzed stratifying for BMI tertiles. Logistic regression was adopted for evaluating the association of HR exceeding the mean value with Il-6 and serum cortisol. According to multivariable linear regression, IL-6 and cortisol levels were associated with HR (B = 1.42, 95% CI = 0.43-2.42; p = .005 and B = .34, 95% CI = 0.17-.51; p < .0001, respectively). The association was significant only among women in the highest BMI tertile (B = 4.16, 95% CI = 1.40-6.91; p = .003 for IL-6 and B = .57, 95% CI = 0.14-1.01; p = .010 for cortisol). Logistic regression confirmed that IL-6 and cortisol levels were associated with HR above the mean value in the highest BMI tertile (OR = 2.13, 95% CI = 1.15-3.97; p = .009 and OR = 1.14, 95% CI = 1.03-1.25; p = .009, respectively). Faster HR is associated with proinflammatory state in elderly patients; this association seems to be limited to women with higher BMI. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 11/2014; · 4.31 Impact Factor
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    ABSTRACT: Excessively elevated resting metabolic rate (RMR) for persons of a certain age, sex, and body composition is a mortality risk factor. Whether elevated RMR constitutes an early marker of health deterioration in older adult has not been fully investigated. Using data from the Baltimore Longitudinal Study of Aging, we hypothesized that higher RMR (i) was cross-sectionally associated with higher multimorbidity and (ii) predicted higher multimorbidity in subsequent follow-ups. The analysis included 695 Baltimore Longitudinal Study of Aging participants, aged 60 or older at baseline, of whom 248 had follow-up data available 2 years later and 109 four years later. Multimorbidity was assessed as number of chronic diseases. RMR was measured by indirect calorimetry and was tested in regression analyses adjusted for covariates age, sex, and dual-energy x-ray absorptiometry-measured total body fat mass and lean mass. Baseline RMR and multimorbidity were positively associated, independent of covariates (p = .002). Moreover, in a three-wave bivariate autoregressive cross-lagged model adjusted for covariates, higher prior RMR predicted greater future multimorbidity above and beyond the cross-sectional and autoregressive associations (p = .034). RMR higher than expected, given age, sex, and body composition, predicts future higher multimorbidity in older adults and may be used as early biomarker of impending health deterioration. Replication and the development of normative data are required for clinical translation. Published by Oxford University Press on behalf of the Gerontological Society of America 2014.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 11/2014; · 4.31 Impact Factor
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    ABSTRACT: In mouse models, CCAAT enhancer-binding protein beta (CEBPB) is necessary for M2 macrophage-mediated regeneration after muscle injury. In humans, CEBPB expression in blood was strongly associated with muscle strength. In this study we aimed to test whether CEBPB expression in blood in people is increased 2 days after exercise designed to induce muscle damage and subsequent repair. Sixteen healthy male volunteers undertook elbow flexor exercises designed to induce acute muscle micro-damage. Peripheral blood samples were collected at baseline and days 1, 2, 4 and 7 following exercise. Expression of CEBPB and related genes were analysed by qRT-PCR. Extent of muscle damage was determined by decline in maximal voluntary isometric torque and by plasma creatine kinase activity. Nine subjects had peak (day 4) creatine kinase activity exceeding 10,000 U/l. In this subgroup, CEBPB expression was elevated from baseline to 2 days post exercise (paired-samples t (1,8) = 3.72, p = 0.006). Related expression and selected cytokine changes after exercise did not reach significance. Muscle-damaging exercise in humans can be followed by induction of CEBPB transcript expression in peripheral blood. Associations between CEBPB expression in blood and muscle strength may be consistent with the CEBPB-dependent muscle repair process.
    The journal of physiological sciences : JPS. 11/2014;
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    ABSTRACT: Persons with diabetes have accelerated muscle loss compared with their counterparts. The relationship of hyperglycemia per se to declines in muscle function has not been explored, yet has implications for developing appropriate intervention strategies to prevent muscle loss.
    Diabetes care. 11/2014;
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    ABSTRACT: Initial results from sequencing studies suggest that there are relatively few low frequency (<5%) variants associated with large effects on common phenotypes. We performed low pass whole genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: i) that sequencing would detect single low frequency - large effect variants that explained similar amounts of phenotypic variance as single common variants, and ii) that some common variant associations could be explained by low frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant - common phenotype associations - 11,132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11,657,229 high quality variants of which 6,129,221 and 5,528,008 were common and low frequency (<5%) respectively, low frequency - large effect associations comprised 7% of detectable cis-gene expression traits (89 of 1,314 cis-eQTLs at P<1x10(-06) (FDR ∼5%)) and 1 of 8 biomarker associations at P<8x10(-10). Very few (30 of 1,232; 2%) common variant associations were fully explained by low frequency variants. Our data show that whole genome sequencing can identify low frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low frequency variants of large effect.
    Human Molecular Genetics 11/2014; · 7.69 Impact Factor
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    ABSTRACT: Aging is a contributing factor in cancer occurrence. We recently demonstrated that systemic immunotherapy (IT) administration in aged, but not young, mice resulted in induction of rapid and lethal cytokine storm. We found that aging was accompanied by increases in visceral fat similar to that seen in young obese (ob/ob or diet-induced obese [DIO]) mice. Yet, the effects of aging and obesity on inflammatory responses to immunotherapeutics are not well defined. We determine the effects of adiposity on systemic IT tolerance in aged compared with young obese mice. Both young ob/ob- and DIO-generated proinflammatory cytokine levels and organ pathologies are comparable to those in aged ad libitum mice after IT, culminating in lethality. Young obese mice exhibited greater ratios of M1/M2 macrophages within the peritoneal and visceral adipose tissues and higher percentages of TNF(+) macrophages in response to αCD40/IL-2 as compared with young lean mice. Macrophage depletion or TNF blockade in conjunction with αCD40/IL-2 prevented cytokine storms in young obese mice and protected from lethality. Calorie-restricted aged mice contain less visceral fat and displayed reduced cytokine levels, protection from organ pathology, and protection from lethality upon αCD40/IL-2 administration. Our data demonstrate that adiposity is a critical factor in the age-associated pathological responses to systemic anti-cancer IT.
    The Journal of experimental medicine. 11/2014;
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    ABSTRACT: Objective We used magnetic resonance imaging (MRI) to study the prevalence and associated clinical characteristics of high-risk plaque (defined as presence of lipid-rich necrotic core [LRNC] and intraplaque hemorrhage) in the superficial femoral arteries (SFA) among people with peripheral artery disease (PAD). Background The prevalence and clinical characteristics associated with high-risk plaque in the SFA are unknown. Methods Three-hundred-three participants with PAD underwent MRI of the proximal SFA using a 1.5 T S platform. Twelve contiguous 2.5 mm cross-sectional images were obtained. Results LRNC was present in 68 (22.4%) participants. Only one had intra-plaque hemorrhage. After adjusting for age and sex, smoking prevalence was higher among adults with LRNC than among those without LRNC (35.9% vs. 21.4%, p = 0.02). Among participants with vs. without LRNC there were no differences in mean percent lumen area (31% vs. 33%, p = 0.42), normalized mean wall area (0.71 vs. 0.70, p = 0.67) or maximum wall area (0.96 vs. 0.92, p = 0.54) in the SFA. Among participants with LRNC, cross-sectional images containing LRNC had a smaller percent lumen area (33% ± 1% vs. 39% ± 1%, p < 0.001), greater normalized mean wall thickness (0.25 ± 0.01 vs. 0.22 ± 0.01, p < 0.001), and greater normalized maximum wall thickness (0.41 ± 0.01 vs. 0.31 ± 0.01, p < 0.001), compared to cross-sectional images without LRNC. Conclusions Fewer than 25% of adults with PAD had high-risk plaque in the proximal SFA using MRI. Smoking was the only clinical characteristic associated with presence of LRNC. Further study is needed to determine the prognostic significance of LRNC in the SFA. Clinical trial registration—URL http://www.clinicaltrials.gov. Unique identifier: NCT00520312.
    Atherosclerosis 11/2014; 237(1):169–176. · 3.71 Impact Factor
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    ABSTRACT: Objective measurement of physical activity using wearable devices such as accelerometers may provide tantalizing new insights into the association between activity and health outcomes. Accelerometers can record quasi-continuous activity information for many days and for hundreds of individuals. For example, in the Baltimore Longitudinal Study on Aging physical activity was recorded every minute for [Formula: see text] adults for an average of [Formula: see text] days per adult. An important scientific problem is to separate and quantify the systematic and random circadian patterns of physical activity as functions of time of day, age, and gender. To capture the systematic circadian pattern, we introduce a practical bivariate smoother and two crucial innovations: (i) estimating the smoothing parameter using leave-one-subject-out cross validation to account for within-subject correlation and (ii) introducing fast computational techniques that overcome problems both with the size of the data and with the cross-validation approach to smoothing. The age-dependent random patterns are analyzed by a new functional principal component analysis that incorporates both covariate dependence and multilevel structure. For the analysis, we propose a practical and very fast trivariate spline smoother to estimate covariate-dependent covariances and their spectra. Results reveal several interesting, previously unknown, circadian patterns associated with human aging and gender.
    Biostatistics (Oxford, England). 10/2014;
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    ABSTRACT: Mechanistic and evolutionary perspectives both agree that aging involves multiple integrated biochemical networks in the organism. In particular, the homeostatic physiological dysregulation (PD) hypothesis contends that aging is caused by the progressive breakdown of key regulatory processes. However, nothing is yet known about the specifics of how PD changes with age and affects health. Using a recently validated measure of PD involving the calculation of a multivariate distance (DM) from biomarker data, we show that PD trajectories predict mortality, frailty, and chronic diseases (cancer, cardiovascular diseases, and diabetes). Specifically, relative risks of outcomes associated with individual slopes in (i.e. rate of) dysregulation range 1.20–1.40 per unit slope. We confirm the results by replicating the analysis using two suites of biomarkers selected with markedly different criteria and, for mortality, in three longitudinal cohort-based studies. Overall, the consistence of effect sizes (direction and magnitude) across data sets, biomarker suites and outcomes suggests that the positive relationship between DM and health outcomes is a general phenomenon found across human populations. Therefore, the study of dysregulation trajectories should allow important insights into aging physiology and provide clinically meaningful predictors of outcomes.
    Mechanisms of Ageing and Development. 10/2014;
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    ABSTRACT: Current operational definitions of sarcopenia are based on algorithms' simultaneous considering measures of skeletal muscle mass and muscle-specific as well as global function. We hypothesize that quantitative and qualitative sarcopenia-related parameters may not be equally predictive of incident disability, thus presenting different clinical relevance.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 10/2014; · 4.31 Impact Factor
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    ABSTRACT: Background Circulating interleukin-6 levels increase with advancing age and are a risk factor for various diseases and mortality. The characterization of gene expression profiles associated with interleukin-6 levels might suggest important molecular events underlying its regulation. Methods and results We studied the association of transcriptional profiles with interleukin-6 levels in 2422 participants from the Framingham Heart Study Offspring Cohort using Affymetrix Human Exon 1.0 ST Array. We identified 4139 genes that were significantly associated with interleukin-6 levels (FDR < 0.05) after adjusting for age, sex and blood cell components. We then replicated 807 genes in the InCHIANTI study with 694 participants. Many of the top genes are involved in inflammation-related pathways or erythrocyte function, including JAK/Stat signaling pathway and interleukin-10 signaling pathway. Conclusion We identified and replicated 807 genes that were associated with circulating interleukin-6 levels. Future characterization of interleukin-6 regulation networks may facilitate the identification of additional potential targets for treating inflammation-related diseases.
    Genomics 10/2014; · 3.01 Impact Factor
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    Neurobiology of Aging 10/2014; 35(10):e25. · 6.17 Impact Factor
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    ABSTRACT: Arising from G. Hemani et al. 508, 249-253 (2014); doi:10.1038/nature13005Epistasis occurs when the effect of a genetic variant on a trait is dependent on genotypes of other variants elsewhere in the genome. Hemani et al. recently reported the detection and replication of many instances of epistasis between pairs of variants influencing gene expression levels in humans. Using whole-genome sequencing data from 450 individuals we strongly replicated many of the reported interactions but, in each case, a single third variant captured by our sequencing data could explain all of the apparent epistasis. Our results provide an alternative explanation for the apparent epistasis observed for gene expression in humans. There is a Reply to this Brief Communication Arising by Hemani, G. et al. Nature 514, http://dx.doi.org/10.1038/nature13692 (2014).
    Nature 10/2014; 514(7520):E3-5. · 38.60 Impact Factor
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    ABSTRACT: Sphingomyelin metabolism has been linked to several diseases and to longevity. However, few epidemiological studies have quantified individual plasma sphingomyelin species (identified by acyl-chain length and saturation) or their relationship between demographic factors and disease processes. In this study, we determined plasma concentrations of distinct sphingomyelin species in 992 individuals, aged 55 and older, enrolled in the Baltimore Longitudinal Study of Aging. Participants were followed, with serial measures, up to 6 visits and 38 years (3972 total samples). Quantitative analyses were performed on a high-performance liquid chromatography-coupled electrospray ionization tandem mass spectrometer. Linear mixed models were used to assess variation in specific sphingomyelin species and associations with demographics, diseases, medications or lifestyle factors, and plasma cholesterol and triglyceride levels. We found that most sphingomyelin species increased with age. Women had higher plasma levels of all sphingomyelin species and showed steeper trajectories of age-related increases compared to men. African Americans also showed higher circulating sphingomyelin concentrations compared to Caucasians. Diabetes, smoking, and plasma triglycerides were associated with lower levels of many sphingomyelins and dihydrosphingomyelins. Notably, these associations showed specificity to sphingomyelin acyl-chain length and saturation. These results demonstrate that longitudinal changes in circulating sphingomyelin levels are influenced by age, sex, race, lifestyle factors, and diseases. It will be important to further establish the intra-individual age- and sex-specific changes in each sphingomyelin species in relation to disease onset and progression.
    Aging cell 10/2014; · 7.55 Impact Factor
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    ABSTRACT: The Multidimensional Prognostic Index (MPI) is a validated predictive tool for long-term mortality based on information collected in a standardized Comprehensive Geriatric Assessment. We investigated whether the MPI is an effective predictor of intrahospital mortality and length of hospital stay after admission to acute geriatric wards.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 09/2014; · 4.31 Impact Factor
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    ABSTRACT: The genetic contribution to longevity in humans has been estimated to range from 15% to 25%. Only two genes, APOE and FOXO3, have shown association with longevity in multiple independent studies.
    The Journals of Gerontology Series A Biological Sciences and Medical Sciences 09/2014; · 4.31 Impact Factor

Publication Stats

28k Citations
5,562.81 Total Impact Points

Institutions

  • 2014
    • California Pacific Medical Center Research Institute
      • Research Institute
      San Francisco, California, United States
  • 2002–2014
    • National Institute on Aging
      • • Clinical Research Branch (CRB)
      • • Laboratory of Cardiovascular Science (LCS)
      • • Laboratory of Personality and Cognition (LPC)
      • • Laboratory of Epidemiology, Demography and Biometry (LEDB)
      Baltimore, Maryland, United States
    • Laval University
      • Département de Médecine Sociale et Préventive
      Québec, Quebec, Canada
  • 2013
    • Delaware State University
      Dover, Delaware, United States
    • Università degli Studi di Salerno
      Fisciano, Campania, Italy
    • Florida State University
      Tallahassee, Florida, United States
    • University of Illinois at Chicago
      Chicago, Illinois, United States
    • Eunice Kennedy Shriver National Institute of Child Health and Human Development
      Maryland, United States
    • University of Turku
      • Department of Public Health
      Turku, Province of Western Finland, Finland
  • 2012–2013
    • Harvard Medical School
      Boston, Massachusetts, United States
    • University-Hospital of Padova
      Padua, Veneto, Italy
    • Università degli Studi di Genova
      • Dipartimento di Medicina sperimentale (DIMES)
      Genova, Liguria, Italy
    • McGill University
      • Department of Epidemiology, Biostatistics and Occupational Health
      Montréal, Quebec, Canada
    • University of Missouri - St. Louis
      Saint Louis, Michigan, United States
  • 2011–2013
    • University of Barcelona
      • Department of Physiology
      Barcelona, Catalonia, Spain
    • University of Geneva
      • Department of Rehabilitation and Geriatrics
      Genève, GE, Switzerland
    • Medizinische Universität Innsbruck
      • Sektion für Genetische Epidemiologie
      Innsbruck, Tyrol, Austria
    • Università degli Studi di Brescia
      Brescia, Lombardy, Italy
    • University of Leicester
      • Department of Health Sciences
      Leicester, ENG, United Kingdom
    • Northeastern University
      • Department of Health Sciences
      Boston, MA, United States
    • Yale University
      New Haven, Connecticut, United States
    • National Institute for Health and Welfare, Finland
      • Department of Health, Functional Capacity and Welfare
      Helsinki, Province of Southern Finland, Finland
  • 2010–2013
    • Queen's University
      • School of Rehabilitation Therapy
      Kingston, Ontario, Canada
    • Pennington Biomedical Research Center
      Baton Rouge, Louisiana, United States
    • University of Maryland, Baltimore County
      • Department of Psychology
      Baltimore, MD, United States
    • Kent State University
      • Department of Psychology
      Kent, OH, United States
  • 2008–2013
    • University of Exeter
      • Peninsula College of Medicine and Dentistry
      Exeter, ENG, United Kingdom
    • University Hospital of Parma
      Parma, Emilia-Romagna, Italy
    • University of California, San Diego
      • Department of Family and Preventive Medicine
      San Diego, CA, United States
    • Saint Louis University
      Saint Louis, Michigan, United States
    • University of Bristol
      • The Henry Wellcome Laboratories for Integrative Neuroscience and Endocrinology
      Bristol, ENG, United Kingdom
    • Boston University
      • Department of Pediatrics
      Boston, MA, United States
    • Harbor-UCLA Medical Center
      Torrance, California, United States
  • 2007–2013
    • LIUCBM Libera Università Campus Bio-Medico di Roma
      • Endocrinology and Diabetes Unit
      Roma, Latium, Italy
    • The Peninsula College of Medicine and Dentistry
      Plymouth, England, United Kingdom
    • Unità Locale Socio Sanitaria Padova ULSS 16
      Padua, Veneto, Italy
    • Duke University Medical Center
      Durham, North Carolina, United States
  • 2006–2013
    • University of Pennsylvania
      • • Department of Radiology
      • • Center for Clinical Epidemiology and Biostatistics
      Philadelphia, Pennsylvania, United States
    • CUNY Graduate Center
      New York City, New York, United States
    • Hospital of the University of Pennsylvania
      • Division of Endocrinology Diabetes and Metabolism
      Philadelphia, Pennsylvania, United States
    • Wake Forest University
      • Department of Health and Exercise Science
      Winston-Salem, NC, United States
    • University of Maryland, College Park
      • Department of Kinesiology
      College Park, MD, United States
  • 2004–2013
    • Universita degli studi di Ferrara
      • • Section of Internal Medicine, Gerontology and Geriatrics
      • • Department of Morphology, Surgery and Experimental Medicine
      Ferrare, Emilia-Romagna, Italy
    • Università degli Studi di Sassari
      Sassari, Sardinia, Italy
  • 2011–2012
    • Chonnam National University
      • School of Mechanical Systems Engineering
      Yeoju, Gyeonggi, South Korea
  • 2010–2012
    • University of Michigan
      • • School of Social Work
      • • Department of Biostatistics
      Ann Arbor, MI, United States
    • University of Mississippi Medical Center
      • School of Medicine
      Jackson, MS, United States
    • IRCCS Ospedale Casa Sollievo della Sofferenza
      • • Department of Geriatrics
      • • Department of Medical Sciences
      San Giovanni Rotondo, Apulia, Italy
  • 2009–2012
    • University of Pittsburgh
      • • Department of Epidemiology
      • • School of Medicine
      • • Division of Geriatric Medicine
      Pittsburgh, PA, United States
    • Kansas City VA Medical Center
      Kansas City, Missouri, United States
    • Yale-New Haven Hospital
      New Haven, Connecticut, United States
    • Rehabilitation Institute of Chicago
      Chicago, Illinois, United States
    • Case Western Reserve University
      • Department of Medicine (University Hospitals Case Medical Center)
      Cleveland, OH, United States
    • National University Hospital of Iceland
      Reikiavik, Capital Region, Iceland
    • Second University of Naples
      Caserta, Campania, Italy
    • University of Southern California
      Los Angeles, California, United States
    • University Hospital Regensburg
      Ratisbon, Bavaria, Germany
    • National Eye Institute
      Maryland, United States
    • MedStar Health Research Institute
      Maryland, United States
  • 2007–2012
    • VU University Medical Center
      • Department of Psychiatry
      Amsterdam, North Holland, Netherlands
  • 2006–2012
    • Johns Hopkins Medicine
      • Department of Urology
      Baltimore, Maryland, United States
  • 2004–2012
    • Johns Hopkins University
      • • Department of Medicine
      • • Division of Geriatric Medicine and Gerontology
      Baltimore, MD, United States
  • 2003–2012
    • National Institutes of Health
      • • Clinical Research Branch (CRB)
      • • Laboratory of Epidemiology, Demography, and Biometry (LEDB)
      Maryland, United States
    • University of Maryland, Baltimore
      • • Department of Epidemiology and Public Health
      • • Department of Medicine
      Baltimore, MD, United States
    • AMC Health
      New York City, New York, United States
    • INRIM Istituto Nazionale di Ricerca Metrologica
      Torino, Piedmont, Italy
    • Spaulding Rehabilitation Hospital
      • Department of Physical Medicine and Rehabilitation
      Boston, MA, United States
  • 2002–2012
    • Northwestern University
      • • Department of Medicine
      • • Feinberg School of Medicine
      • • Department of Preventive Medicine
      Evanston, IL, United States
  • 2010–2011
    • University of California, San Francisco
      • Division of Hospital Medicine
      San Francisco, CA, United States
  • 2008–2011
    • University of Delaware
      • Department of Physical Therapy
      Newark, DE, United States
  • 2006–2011
    • Università degli Studi G. d'Annunzio Chieti e Pescara
      • Center for Aging Sciences CESI
      Chieti, Abruzzo, Italy
    • Cornell University
      • • Department of Human Development
      • • Department of Nutritional Sciences
      Ithaca, NY, United States
  • 2004–2011
    • Università degli studi di Parma
      • Department of Clinical and Experimental Medicine
      Parma, Emilia-Romagna, Italy
  • 2006–2010
    • Azienda Sanitaria di Firenze
      Florens, Tuscany, Italy
  • 2006–2009
    • University of Florida
      • Department of Aging and Geriatric Research
      Gainesville, FL, United States
    • Fondazione Don Carlo Gnocchi
      Milano, Lombardy, Italy
  • 2005–2009
    • Università degli Studi di Perugia
      • Department of Clinical and Experimental Medicine
      Perugia, Umbria, Italy
    • Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele Pisana
      Roma, Latium, Italy
    • Centers for Disease Control and Prevention
      • National Center for Health Statistics
      Druid Hills, GA, United States
    • Università Vita-Salute San Raffaele
      Milano, Lombardy, Italy
    • Johnson & Johnson
      New Brunswick, New Jersey, United States
    • IMIM Hospital del Mar Medical Research Institute
      Barcino, Catalonia, Spain
  • 2004–2009
    • Johns Hopkins Bloomberg School of Public Health
      • • Department of Biostatistics
      • • Johns Hopkins Center on Aging and Health
      Baltimore, MD, United States
  • 1996–2009
    • INRCA Istituto Nazionale di Ricovero e Cura per Anziani
      Ancona, The Marches, Italy
  • 2003–2008
    • Università degli studi di Palermo
      • Dipartimento di Discipline Chirurgiche, Oncologiche e Stomatologiche (Di.Chir.On.S.)
      Palermo, Sicily, Italy
  • 2002–2007
    • University of Washington Seattle
      • Department of Rehabilitation Medicine
      Seattle, WA, United States
  • 2003–2005
    • Catholic University of the Sacred Heart
      • School of Geriatrics
      Roma, Latium, Italy
    • University of Naples Federico II
      Napoli, Campania, Italy
    • Wake Forest School of Medicine
      • • Division of Public Health Sciences
      • • Sticht Center on Aging
      Winston-Salem, NC, United States
  • 1986–2005
    • University of Florence
      • Dipartimento di Chirurgia e Medicina Traslazionale (DCMT)
      Florence, Tuscany, Italy