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1. Smart Scattering Scanning Near-Field Optical Microscopy

Author

Labouesse, S, Johnson, SC, Bechtel, HA, Raschke, MB, and Piestun, R

Subjects
Optical Physics, Quantum Physics, Electrical and Electronic Engineering
Abstract

Scattering scanning near-field optical microscopy (s-SNOM) provides spectroscopic imaging from molecular to quantum materials with few nanometer deep subdiffraction limited spatial resolution. However, in its conventional implementation s-SNOM is slow to effectively acquire a series of spatio-spectral images, especially with large fields of view. This problem is further exacerbated for weak resonance contrast or when using light sources with limited spectral irradiance. Indeed, the generally limited signal-to-noise ratio prevents sampling a weak signal at the Nyquist sampling rate. Here, we demonstrate how acquisition time and sampling rate can be significantly reduced by using compressed sampling, matrix completion, and adaptive random sampling, while maintaining or even enhancing the physical or chemical image content. We use fully sampled real data sets of molecular, biological, and quantum materials as ground-truth physical data and show how deep under-sampling with a corresponding reduction of acquisition time by 1 order of magnitude or more retains the core s-SNOM image information. We demonstrate that a sampling rate of up to 6× smaller than the Nyquist criterion can be applied, which would provide a 30-fold reduction in the data required under typical experimental conditions. Our smart s-SNOM approach is generally applicable and provides systematic full spatio-spectral s-SNOM imaging with a large field of view at high spectral resolution and reduced acquisition time.

Published
2020

2. Synchrotron infrared nano-spectroscopy and -imaging

Author

Bechtel, HA, Johnson, SC, Khatib, O, Muller, EA, and Raschke, MB

Subjects
Chemical Physics, Condensed Matter Physics, Physical Chemistry, Physical Chemistry (incl. Structural)
Abstract

Infrared (IR) spectroscopy has evolved into a powerful analytical technique to probe molecular and lattice vibrations, low-energy electronic excitations and correlations, and related collective surface plasmon, phonon, or other polaritonic resonances. In combination with scanning probe microscopy, near-field infrared nano-spectroscopy and -imaging techniques have recently emerged as a frontier in imaging science, enabling the study of complex heterogeneous materials with simultaneous nanoscale spatial resolution and chemical and quantum state spectroscopic specificity. Here, we describe synchrotron infrared nano-spectroscopy (SINS), which takes advantage of the low-noise, broadband, high spectral irradiance, and coherence of synchrotron infrared radiation for near-field infrared measurements across the mid- to far-infrared with nanometer spatial resolution. This powerful combination provides a qualitatively new form of broadband spatio-spectral analysis of nanoscale, mesoscale, and surface phenomena that were previously difficult to study with IR techniques, or even any form of micro-spectroscopy in general. We review the development of SINS, describe its technical implementations, and highlight selected examples representative of the rapidly growing range of applications in physics, chemistry, biology, materials science, geology, and atmospheric and space sciences.

Published
2020

3. Harmonizing tau positron emission tomography in Alzheimer's disease: The CenTauR scale and the joint propagation model.

Author

Leuzy, A, Raket, LL, Villemagne, VL, Klein, G, Tonietto, M, Olafson, E, Baker, S, Saad, ZS, Bullich, S, Lopresti, B, Bohorquez, SS, Boada, M, Betthauser, TJ, Charil, A, Collins, EC, Collins, JA, Cullen, N, Gunn, RN, Higuchi, M, Hostetler, E, Hutchison, RM, Iaccarino, L, Insel, PS, Irizarry, MC, Jack, CR, Jagust, WJ, Johnson, KA, Johnson, SC, Karten, Y, Marquié, M, Mathotaarachchi, S, Mintun, MA, Ossenkoppele, R, Pappas, I, Petersen, RC, Rabinovici, GD, Rosa-Neto, P, Schwarz, CG, Smith, R, Stephens, AW, Whittington, A, Carrillo, MC, Pontecorvo, MJ, Haeberlein, SB, Dunn, B, Kolb, HC, Sivakumaran, S, Rowe, CC, Hansson, O, Doré, V, Leuzy, A, Raket, LL, Villemagne, VL, Klein, G, Tonietto, M, Olafson, E, Baker, S, Saad, ZS, Bullich, S, Lopresti, B, Bohorquez, SS, Boada, M, Betthauser, TJ, Charil, A, Collins, EC, Collins, JA, Cullen, N, Gunn, RN, Higuchi, M, Hostetler, E, Hutchison, RM, Iaccarino, L, Insel, PS, Irizarry, MC, Jack, CR, Jagust, WJ, Johnson, KA, Johnson, SC, Karten, Y, Marquié, M, Mathotaarachchi, S, Mintun, MA, Ossenkoppele, R, Pappas, I, Petersen, RC, Rabinovici, GD, Rosa-Neto, P, Schwarz, CG, Smith, R, Stephens, AW, Whittington, A, Carrillo, MC, Pontecorvo, MJ, Haeberlein, SB, Dunn, B, Kolb, HC, Sivakumaran, S, Rowe, CC, Hansson, O, and Doré, V

Abstract

INTRODUCTION: Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis. METHODS: Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data. JPM was compared to a linear regression approach analogous to the one used in the amyloid PET Centiloid scale. RESULTS: A strong linear relationship was observed between CTR values across brain regions. Using the JPM approach, CTR estimates were similar to, but more accurate than, those derived using the linear regression approach. DISCUSSION: Preliminary findings using the JPM support the development and adoption of a universal scale for tau-PET quantification. HIGHLIGHTS: Tested a novel joint propagation model (JPM) to harmonize quantification of tau PET. Units of common scale are termed "CenTauRs". Tested a Centiloid-like linear regression approach. Using five cohorts with head-to-head tau PET, JPM outperformed linearregressionbased approach. Strong linear relationship was observed between CenTauRs values across brain regions.

Published
2024

5. The geochemistry of metalliferous black shales : understanding primary enrichments, metamorphic processes, and the role of metal-rich black shales in archiving earth evolution

Author

Johnson, SC

Abstract

Black shales offer an important context to study the evolution of the oceans and atmosphere. As an archive of past climatic, evolutionary and geodynamic events in Earth history they offer the opportunity to further understand the nature of such events. Various redox sensitive and bio-essential trace metals can become enriched in black shales as a function of the nature of the environment in which they were deposited. Of particular interest are shales with high metal enrichment, as they may represent an important geochemical flux. In addition to archiving paleo-ocean and climatic conditions, metal-rich black shales offer important economic targets for hydrocarbon and mineral exploration. Previous work has shown that many important trace metals in black shales are hosted by diagenetic pyrite. Furthermore, metal-rich pyrite has been argued as a potential source for orogenic gold deposits where the metamorphic breakdown of pyrite to pyrrhotite releases gold and other trace metals. This thesis presents a series of studies on aspects of black shale enrichment and mineralogy and offers greater insight as to the evolution of our oceans and atmosphere and how this may relate to secular metal enrichments. Each adopts a multi-proxy, geochemical approach, augmenting pyrite trace element chemistry with isotopic, and whole-rock geochemical or mineralogical data. In addition to addressing the nature of the depositional environment of black shales, mechanisms of ore genesis via the metamorphism of metal-rich shale sequences are also presented and evaluated. Four main aspects of the geochemistry of black shales are investigated in this thesis: 1) primary metal enrichment as a result of the changing nature of the water column during climatic perturbation, 2) the application of pyrite trace element chemistry along with other geochemical techniques to understand, and differentiate, primary metal enrichments and metamorphic overprint, 3) mechanisms by which pyrite may convert to pyrrhotite in black shale and what implications this may have for metal mobility, and 4) understanding of metal enrichments in black shale through geological time in the context of geodynamic and geochemical evolution. The understanding of primary metal enrichments in black shales is critical in order to interpret the nature of the depositional environment. Chapters 2, 3 and 4 offer reviews of metal enrichment in sediments through time, as well as whole rock and pyrite trace element chemistry, as a means to record the chemical nature of the water column at the time of deposition. This is then applied in Chapter 5 to a metalliferous black shale section in NW Estonia of Cambrian to Ordovician age. This work contains a new detailed sedimentological study of the section to establish a depositional context. This is augmented by a high-resolution multi-proxy data and isotope geochemistry suggests that the section archives a major carbon isotope excursion (the Steptoean Positive Isotope Excursion; SPICE), not previously reported in these rocks, and allows the section to be placed in a global context. The high phosphorous content of the biograinstones deposited prior to the carbon isotope excursion suggests that phosphogenesis may have led to increased bio-productivity, affecting the carbon cycle. The high pyrite content and the geochemistry of this mineral in the lower portion of the black shale, which sits above the isotope excursion, suggests that the SPICE event may have led to the onset of low-oxygen conditions across the Baltica shallow shelf, and the deposition of metal-rich black shales. In Chapter 6, the geodynamic and biochemical processes associated with metal enrichments in the formation metal-rich shales are reviewed and discussed together with a geochemical and mineralogical study of the Talvivaara deposit in Finland. As the world's largest black-shale poly-metallic deposit it offers a context to evaluate the economic aspects of black shale enrichment. The Paleoproterozoic age of this deposit and the fact that there are no known analogues that match its size, suggests that there may have been unique environmental factors that played a role in its metal enrichment. Much like the Estonian example, Talvivaara was deposited in the immediate wake of a large, global carbon isotope excursion. The excursion (the Lomagundi- Jatuli event, ~2.1 Ga) is the largest carbon isotope excursion preserved in the rock record and is followed by the first, and largest, mass burial of organic carbon in Earth history. This suggests that there may be analogous processes operating, but on different scales, in order to facilitate such enrichments. The deposit has been metamorphosed to amphibolite facies with only some primary pyrite, zircon and bituminous organic matter preserved. In-situ ˜í¬•\\(^{34}\\)S-isotopic values of the sulfide phases and LA-ICP-MS analyses of iron sulfide phases, shows that the earliest pyrite (interpreted to be of syn-sedimentary to early-diagenetic in origin) is trace metal rich. The ˜í¬•\\(^{34}\\)S values in these pyrites suggest bacterial sulfate reduction from an open sulfate reservoir. The earliest pyrite is the most metal-rich, suggesting that the original depositional environment facilitated high metal concentrations. The shale package was then metamorphosed releasing trace elements and sulfur that then went on to form their own phases. Chapter 7 presents new data and insights as to the nature of metamorphic reduction of pyrite to pyrrhotite in black shales. Often this transformation is only considered as proceeding at greenschist facies metamorphism (~300¬¨‚àûC, and above). An experimental study, focusing on the magnetic character of pyrite, suggests that the transformation begins at ~177¬¨‚àûC. The lower temperature proposed is supported by LA-ICP-MS studies of pyrite and pyrrhotite in Estonian oil shale and diagenetic to catagenetic sulfide concretions in sub-greenschist facies black shales from NW Russia. A model is presented suggesting that the degradation of organic matter during catagenesis is sufficient in producing enough reducing potential to facilitate the initial stages of the transformation to pyrrhotite. Chapter 8 presents a synthesis of the geochemistry of metal-rich black shales. Some of the most metal-rich black shales worldwide are classified and compared to 'normal' black shales from throughout Earth history. In doing so, a new model is presented which suggests that during the Phanerozoic, plate collisional events led to increased detrital material into basins, leading to increased bio-productivity and atmospheric O\\(_2\\). With increasing O\\(_2\\) in the atmosphere, the processes of weathering and erosion became more vigorous leading to further influx of detritus and blooms of bio-productivity promoting the development of water column anoxia. Eventually, the cycle breaks when uptake by organisms exceeds input from the continents, leading to the return of 'normal' black shales and increasing carbon release to the atmosphere. This process is likely to have been the dominant process during the Phanerozoic, where supercontinent cycles are more dynamic and cover greater surface area. However, the Proterozoic also has some of the largest metalliferous, and organic-rich, sediments in the rock record and do not directly correlate to periods of supercontinent amalgamation. Instead, a discussion is presented that addresses the influence of widespread glaciations as a process to effectively erode, whilst simultaneously release oxygen, leading to increased oxidative weather and bio-productivity in their aftermath.

Published
2023
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6. Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors

Author

Bashyam, VM, Doshi, J, Erus, G, Srinivasan, D, Abdulkadir, A, Singh, A, Habes, M, Fan, Y, Masters, CL, Maruff, P, Zhuo, C, Voelzke, H, Johnson, SC, Fripp, J, Koutsouleris, N, Satterthwaite, TD, Wolf, DH, Gur, RE, Gur, RC, Morris, JC, Albert, MS, Grabe, HJ, Resnick, SM, Bryan, NR, Wittfeld, K, Bulow, R, Wolk, DA, Shou, H, Nasrallah, IM, Davatzikos, C, Bashyam, VM, Doshi, J, Erus, G, Srinivasan, D, Abdulkadir, A, Singh, A, Habes, M, Fan, Y, Masters, CL, Maruff, P, Zhuo, C, Voelzke, H, Johnson, SC, Fripp, J, Koutsouleris, N, Satterthwaite, TD, Wolf, DH, Gur, RE, Gur, RC, Morris, JC, Albert, MS, Grabe, HJ, Resnick, SM, Bryan, NR, Wittfeld, K, Bulow, R, Wolk, DA, Shou, H, Nasrallah, IM, and Davatzikos, C

Abstract

BACKGROUND: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability. PURPOSE: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction. STUDY TYPE: Retrospective. POPULATION: Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites. FIELD STRENGTH/SEQUENCE: Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T. ASSESSMENT: StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data. STATISTICAL TESTS: Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model. RESULTS: Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization. DATA CONCLUSION: While

Published
2022

7. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

Author

Murray, CJL, Ikuta, KS, Sharara, F, Swetschinski, L, Aguilar, GR, Gray, A, Han, C, Bisignano, C, Rao, P, Wool, E, Johnson, SC, Browne, AJ, Chipeta, MG, Fell, F, Hackett, S, Haines-Woodhouse, G, Hamadani, BHK, Kumaran, EAP, McManigal, B, Agarwal, R, Akech, S, Albertson, S, Amuasi, J, Andrews, J, Aravkin, A, Ashley, E, Bailey, F, Baker, S, Basnyat, B, Bekker, A, Bender, R, Bethou, A, Bielicki, J, Boonkasidecha, S, Bukosia, J, Carvalheiro, C, Castaneda-Orjuela, C, Chansamouth, V, Chaurasia, S, Chiurchiu, S, Chowdhury, F, Cook, AJ, Cooper, B, Cressey, TR, Criollo-Mora, E, Cunningham, M, Darboe, S, Day, NPJ, De Luca, M, Dokova, K, Dramowski, A, Dunachie, SJ, Eckmanns, T, Eibach, D, Emami, A, Feasey, N, Fisher-Pearson, N, Forrest, K, Garrett, D, Gastmeier, P, Giref, AZ, Greer, RC, Gupta, V, Haller, S, Haselbeck, A, Hay, S, Holm, M, Hopkins, S, Iregbu, KC, Jacobs, J, Jarovsky, D, Javanmardi, F, Khorana, M, Kissoon, N, Kobeissi, E, Kostyanev, T, Krapp, F, Krumkamp, R, Kumar, A, Kyu, HH, Lim, C, Limmathurotsakul, D, Loftus, MJ, Lunn, M, Ma, J, Mturi, N, Munera-Huertas, T, Musicha, P, Mussi-Pinhata, MM, Nakamura, T, Nanavati, R, Nangia, S, Newton, P, Ngoun, C, Novotney, A, Nwakanma, D, Obiero, CW, Olivas-Martinez, A, Olliaro, P, Ooko, E, Ortiz-Brizuela, E, Peleg, AY, Perrone, C, Plakkal, N, Ponce-de-Leon, A, Raad, M, Ramdin, T, Riddell, A, Roberts, T, VictoriaRobotham, J, Roca, A, Rudd, KE, Russell, N, Schnall, J, Scott, JAG, Shivamallappa, M, Sifuentes-Osornio, J, Steenkeste, N, Stewardson, AJ, Stoeva, T, Tasak, N, Thaiprakong, A, Thwaites, G, Turner, C, Turner, P, van Doorn, HR, Velaphi, S, Vongpradith, A, Huong, V, Walsh, T, Waner, S, Wangrangsimakul, T, Wozniak, T, Zheng, P, Sartorius, B, Lopez, AD, Stergachis, A, Moore, C, Dolecek, C, Naghavi, M, Murray, CJL, Ikuta, KS, Sharara, F, Swetschinski, L, Aguilar, GR, Gray, A, Han, C, Bisignano, C, Rao, P, Wool, E, Johnson, SC, Browne, AJ, Chipeta, MG, Fell, F, Hackett, S, Haines-Woodhouse, G, Hamadani, BHK, Kumaran, EAP, McManigal, B, Agarwal, R, Akech, S, Albertson, S, Amuasi, J, Andrews, J, Aravkin, A, Ashley, E, Bailey, F, Baker, S, Basnyat, B, Bekker, A, Bender, R, Bethou, A, Bielicki, J, Boonkasidecha, S, Bukosia, J, Carvalheiro, C, Castaneda-Orjuela, C, Chansamouth, V, Chaurasia, S, Chiurchiu, S, Chowdhury, F, Cook, AJ, Cooper, B, Cressey, TR, Criollo-Mora, E, Cunningham, M, Darboe, S, Day, NPJ, De Luca, M, Dokova, K, Dramowski, A, Dunachie, SJ, Eckmanns, T, Eibach, D, Emami, A, Feasey, N, Fisher-Pearson, N, Forrest, K, Garrett, D, Gastmeier, P, Giref, AZ, Greer, RC, Gupta, V, Haller, S, Haselbeck, A, Hay, S, Holm, M, Hopkins, S, Iregbu, KC, Jacobs, J, Jarovsky, D, Javanmardi, F, Khorana, M, Kissoon, N, Kobeissi, E, Kostyanev, T, Krapp, F, Krumkamp, R, Kumar, A, Kyu, HH, Lim, C, Limmathurotsakul, D, Loftus, MJ, Lunn, M, Ma, J, Mturi, N, Munera-Huertas, T, Musicha, P, Mussi-Pinhata, MM, Nakamura, T, Nanavati, R, Nangia, S, Newton, P, Ngoun, C, Novotney, A, Nwakanma, D, Obiero, CW, Olivas-Martinez, A, Olliaro, P, Ooko, E, Ortiz-Brizuela, E, Peleg, AY, Perrone, C, Plakkal, N, Ponce-de-Leon, A, Raad, M, Ramdin, T, Riddell, A, Roberts, T, VictoriaRobotham, J, Roca, A, Rudd, KE, Russell, N, Schnall, J, Scott, JAG, Shivamallappa, M, Sifuentes-Osornio, J, Steenkeste, N, Stewardson, AJ, Stoeva, T, Tasak, N, Thaiprakong, A, Thwaites, G, Turner, C, Turner, P, van Doorn, HR, Velaphi, S, Vongpradith, A, Huong, V, Walsh, T, Waner, S, Wangrangsimakul, T, Wozniak, T, Zheng, P, Sartorius, B, Lopez, AD, Stergachis, A, Moore, C, Dolecek, C, and Naghavi, M

Abstract

BACKGROUND: Antimicrobial resistance (AMR) poses a major threat to human health around the world. Previous publications have estimated the effect of AMR on incidence, deaths, hospital length of stay, and health-care costs for specific pathogen-drug combinations in select locations. To our knowledge, this study presents the most comprehensive estimates of AMR burden to date. METHODS: We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with bacterial AMR for 23 pathogens and 88 pathogen-drug combinations in 204 countries and territories in 2019. We obtained data from systematic literature reviews, hospital systems, surveillance systems, and other sources, covering 471 million individual records or isolates and 7585 study-location-years. We used predictive statistical modelling to produce estimates of AMR burden for all locations, including for locations with no data. Our approach can be divided into five broad components: number of deaths where infection played a role, proportion of infectious deaths attributable to a given infectious syndrome, proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antibiotic of interest, and the excess risk of death or duration of an infection associated with this resistance. Using these components, we estimated disease burden based on two counterfactuals: deaths attributable to AMR (based on an alternative scenario in which all drug-resistant infections were replaced by drug-susceptible infections), and deaths associated with AMR (based on an alternative scenario in which all drug-resistant infections were replaced by no infection). We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity. We present final estimates aggregated to the global and regional level. FINDINGS: On the bas

Published
2022

8. The Structured Distance to the Nearest System Without Property P

Author

Johnson, SC, Wicks, M, Žefran, M, and DeCarlo, RA

Subjects
Mechanical Engineering, FOS: Mechanical engineering
Abstract

For a system matrix M, this paper explores the smallest (Frobenius) norm additive structured perturbation δM for which a system property P (e.g., controllability, observability, stability, etc.) fails to hold, i.e., δM is the structured perturbation with smallest Frobenius norm such that there exists a property matrix R ∈ P for which M-δM-R drops rank. The Frobenius norm is used because of its direct dependence on the magnitude of each entry in the perturbation matrix. Necessary conditions on a locally minimum norm structured rank-reducing perturbation δM and associated property matrix R are set forth and proven. An iterative algorithm is also set forth that computes a locally minimum norm structured perturbation and associated property matrix satisfying the necessary conditions. Algorithm convergence is proven using a discrete Lyapunov function.

Published
2021
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9. Sex‐specific genetic predictors of memory performance

Author

Smith, AN, Dumitrescu, L, Mukherjee, S, Lee, ML, Choi, S, Trittschuh, EH, Mez, J, Mahoney, ER, Bush, WS, Engelman, CD, Lu, Q, Fardo, DW, Widaman, KF, Buckley, RF, Mormino, EC, Harrison, TM, Sanders, RE, Clark, LR, Gifford, KA, Vardarajan, BN, Initiative, ADN, Consortium, ADG, Team, AS, Project, TADS, Cuccaro, ML, Pericak‐Vance, MA, Farrer, LA, Wang, L, Schellenberg, GD, Haines, JL, Jefferson, AL, Johnson, SC, Kukull, WA, Albert, MS, Keene, CD, Saykin, AJ, Larson, EB, Sperling, RA, Mayeux, R, Bennett, DA, Schneider, JA, Crane, PK, Hohman, TJ, Smith, AN, Dumitrescu, L, Mukherjee, S, Lee, ML, Choi, S, Trittschuh, EH, Mez, J, Mahoney, ER, Bush, WS, Engelman, CD, Lu, Q, Fardo, DW, Widaman, KF, Buckley, RF, Mormino, EC, Harrison, TM, Sanders, RE, Clark, LR, Gifford, KA, Vardarajan, BN, Initiative, ADN, Consortium, ADG, Team, AS, Project, TADS, Cuccaro, ML, Pericak‐Vance, MA, Farrer, LA, Wang, L, Schellenberg, GD, Haines, JL, Jefferson, AL, Johnson, SC, Kukull, WA, Albert, MS, Keene, CD, Saykin, AJ, Larson, EB, Sperling, RA, Mayeux, R, Bennett, DA, Schneider, JA, Crane, PK, and Hohman, TJ

Abstract

Background There are notable sex differences in the clinical manifestation of Alzheimer’s disease (AD), including differences in baseline and longitudinal changes in memory performance. However, sex-stratified models have not been routinely incorporated into genetic studies of cognitive performance or decline. We sought to identify sex-specific genetic predictors of memory performance in aging adults. Method We obtained harmonized memory scores from 11,601 females and 8,885 males from 7 cohorts of cognitive aging. We calculated scores using latent variable modeling techniques anchored on overlapping neuropsychological test items across datasets to perform both sex-stratified and sex-interaction genome-wide association studies in each cohort using mixed-effects regression models to assess genetic associations with baseline memory score and rate of memory decline. Covariates included age at baseline and population principal components. We then combined results in a fixed-effect meta-analysis. Results As expected, we observed robust associations at the APOE locus in males and females in both baseline and longitudinal models. Additionally, we identified a sex-specific locus for longitudinal memory performance (interaction-p=2.54e-6) on chromosome 7 within LINC-PINT that was genome-wide significant in males (index SNP= rs66754621; p=1.62e-8; MAF=0.20), but not females (p=0.65). This variant is in an enhancer region within several brain regions, as well as within a promoter-anchored chromatin interaction loop and a methylation eQTL in the prefrontal cortex. No female-specific loci were identified. Interestingly, we did observe nominal associations in baseline memory at some known AD loci, including male-specific effects at MS4A6A, FERMT2, and KAT8 (interaction ps=0.0026, 0.027, and 0.0058, respectively) and opposing effects between males and females at INPP5D (interaction p=0.0021). Conclusion These results highlight a novel male-specific locus of memory performance on ch

Published
2021

10. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study

Author

Rudd, KE, Johnson, SC, Agesa, KM, Shackelford, KA, Tsoi, D, Kievlan, DR, Colombara, DV, Ikuta, KS, Kissoon, N, Finfer, S, Fleischmann-Struzek, C, Machado, FR, Reinhart, KK, Rowan, K, Seymour, CW, Watson, RS, West, TE, Marinho, F, Hay, SI, Lozano, R, Lopez, AD, Angus, DC, Murray, CJL, Naghavi, M, Rudd, KE, Johnson, SC, Agesa, KM, Shackelford, KA, Tsoi, D, Kievlan, DR, Colombara, DV, Ikuta, KS, Kissoon, N, Finfer, S, Fleischmann-Struzek, C, Machado, FR, Reinhart, KK, Rowan, K, Seymour, CW, Watson, RS, West, TE, Marinho, F, Hay, SI, Lozano, R, Lopez, AD, Angus, DC, Murray, CJL, and Naghavi, M

Abstract

BACKGROUND: Sepsis is life-threatening organ dysfunction due to a dysregulated host response to infection. It is considered a major cause of health loss, but data for the global burden of sepsis are limited. As a syndrome caused by underlying infection, sepsis is not part of standard Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) estimates. Accurate estimates are important to inform and monitor health policy interventions, allocation of resources, and clinical treatment initiatives. We estimated the global, regional, and national incidence of sepsis and mortality from this disorder using data from GBD 2017. METHODS: We used multiple cause-of-death data from 109 million individual death records to calculate mortality related to sepsis among each of the 282 underlying causes of death in GBD 2017. The percentage of sepsis-related deaths by underlying GBD cause in each location worldwide was modelled using mixed-effects linear regression. Sepsis-related mortality for each age group, sex, location, GBD cause, and year (1990-2017) was estimated by applying modelled cause-specific fractions to GBD 2017 cause-of-death estimates. We used data for 8·7 million individual hospital records to calculate in-hospital sepsis-associated case-fatality, stratified by underlying GBD cause. In-hospital sepsis-associated case-fatality was modelled for each location using linear regression, and sepsis incidence was estimated by applying modelled case-fatality to sepsis-related mortality estimates. FINDINGS: In 2017, an estimated 48·9 million (95% uncertainty interval [UI] 38·9-62·9) incident cases of sepsis were recorded worldwide and 11·0 million (10·1-12·0) sepsis-related deaths were reported, representing 19·7% (18·2-21·4) of all global deaths. Age-standardised sepsis incidence fell by 37·0% (95% UI 11·8-54·5) and mortality decreased by 52·8% (47·7-57·5) from 1990 to 2017. Sepsis incidence and mortality varied substantially across regions, with the highest burden in s

Published
2020

12. Relationship of amyloid beta and neurofibrillary tau deposition in Neurodegeneration in Aging Down Syndrome (NiAD) study at baseline

Author

Tudorascu, DL, primary, Laymon, CM, additional, Zammit, M, additional, Minhas, DS, additional, Anderson, SJ, additional, Ellison, PA, additional, Zaman, S, additional, Ances, BM, additional, Sabbagh, M, additional, Johnson, SC, additional, Mathis, CA, additional, Klunk, WE, additional, Handen, BL, additional, Christian, BT, additional, and Cohen, AD, additional

Published
2020
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15. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of Disease Study 2015

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Barber, RM, Fullman, N, Sorensen, RJD, Bollyky, T, McKee, M, Nolte, E, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulle, AM, Abdurahman, AA, Abera, SF, Abraham, B, Abreha, GF, Adane, K, Adelekan, AL, Adetifa, IMO, Afshin, A, Agarwal, A, Agarwal, SK, Agarwal, S, Agrawal, A, Kiadaliri, AA, Ahmadi, A, Ahmed, KY, Ahmed, MB, Akinyemi, RO, Akinyemiju, TF, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alam, SS, Alemu, ZA, Alene, KA, Alexander, L, Ali, R, Ali, SD, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Amo-Adjei, J, Amoako, YA, Anderson, BO, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Ärnlöv, J, Artaman, A, Asayesh, H, Assadi, R, Astatkie, A, Atey, TM, Atique, S, Atnafu, NT, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Quintanilla, BPA, Awasthi, A, Ayele, NN, Azzopardi, P, Saleem, HOB, Bärnighausen, T, Bacha, U, Badawi, A, Banerjee, A, Barac, A, Barboza, MA, Barker-Collo, SL, Barrero, LH, Basu, S, Baune, BT, Baye, K, Bayou, YT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Béjot, Y, Bello, AK, Bennett, DA, Bensenor, IM, Berhane, A, Bernabé, E, Bernal, OA, Beyene, AS, Beyene, TJ, Bhutta, ZA, Biadgilign, S, Bikbov, B, Birlik, SM, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bose, D, Brainin, M, Brauer, M, Brazinova, A, Breitborde, NJK, Brenner, H, Butt, ZA, Cárdenas, R, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Carrero, JJ, Casey, D, Caso, V, Castañeda-Orjuela, CA, Rivas, JC, Catalá-López, F, Cecilio, P, Cercy, K, Charlson, FJ, Chen, AZ, Chew, A, Chibalabala, M, Chibueze, CE, Chisumpa, VH, Chitheer, AA, Chowdhury, R, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Coggeshall, MS, Cooper, LT, Cortinovis, M, Crump, JA, Dalal, K, Danawi, H, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, De Leo, D, Del Gobbo, LC, del Pozo-Cruz, B, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dicker, D, Ding, EL, Dokova, K, Dorsey, ER, Doyle, KE, Dubey, M, Ehrenkranz, R, Ellingsen, CL, Elyazar, I, Enayati, A, Ermakov, SP, Eshrati, B, Esteghamati, A, Estep, K, Fürst, T, Faghmous, IDA, Fanuel, FBB, Faraon, EJA, Farid, TA, Farinha, CSES, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Feigl, AB, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Fischer, F, Fitzmaurice, C, Fleming, TD, Foigt, N, Foreman, KJ, Forouzanfar, MH, Franklin, RC, Frostad, J, G/hiwot, TT, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gao, W, Garcia-Basteiro, AL, Gebre, T, Gebremedhin, AT, Gebremichael, MW, Gebru, AA, Gelaye, AA, Geleijnse, JM, Genova-Maleras, R, Gibney, KB, Giref, AZ, Gishu, MD, Giussani, G, Godwin, WW, Gold, A, Goldberg, EM, Gona, PN, Goodridge, A, Gopalani, SV, Goto, A, Graetz, N, Greaves, F, Griswold, M, Guban, PI, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Habtewold, TD, Hafezi-Nejad, N, Haile, D, Hailu, AD, Hailu, GB, Hakuzimana, A, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, Hendrie, D, Heredia-Pi, IB, Hoek, HW, Horino, M, Horita, N, Hosgood, HD, Htet, AS, Hu, G, Huang, H, Huang, JJ, Huntley, BM, Huynh, C, Iburg, KM, Ileanu, BV, Innos, K, Irenso, AA, Jahanmehr, N, Jakovljevic, MB, James, P, James, SL, Javanbakht, M, Jayaraman, SP, Jayatilleke, AU, Jeemon, P, Jha, V, John, D, Johnson, C, Johnson, SC, Jonas, JB, Juel, K, Kabir, Z, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Kasaeian, A, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kazanjan, K, Keiyoro, PN, Kemmer, L, Kemp, AH, Kengne, AP, Kerbo, AA, Kereselidze, M, Kesavachandran, CN, Khader, YS, Khalil, I, Khan, AR, Khan, EA, Khan, G, Khang, Y-H, Khoja, ATA, Khonelidze, I, Khubchandani, J, Kibret, GD, Kim, D, Kim, P, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kissoon, N, Kivipelto, M, Kokubo, Y, Kolk, A, Kolte, D, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krishnaswami, S, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulkarni, VS, Kumar, GA, Kumsa, FA, Kutz, M, Kyu, HH, Lager, ACJ, Lal, A, Lal, DK, Lalloo, R, Lallukka, T, Lan, Q, Langan, SM, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Latif, AA, Lawrynowicz, AEB, Leasher, JL, Leigh, J, Leinsalu, M, Leshargie, CT, Leung, J, Leung, R, Levi, M, Liang, X, Lim, SS, Lind, M, Linn, S, Lipshultz, SE, Liu, P, Liu, Y, Lo, L-T, Logroscino, G, Lopez, AD, Lorch, SA, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Macarayan, ERK, Mackay, MT, El Razek, HMA, El Razek, MMA, Mahdavi, M, Majeed, A, Malekzadeh, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marcenes, W, Marks, GB, Marquez, N, Martinez-Raga, J, Marzan, MB, Massano, J, Mathur, MR, Maulik, PK, Mazidi, M, McAlinden, C, McGrath, JJ, McNellan, C, Meaney, PA, Mehari, A, Mehndiratta, MM, Meier, T, Mekonnen, AB, Meles, KG, Memish, ZA, Mengesha, MM, Mengiste, DT, Mengistie, MA, Menota, BG, Mensah, GA, Mereta, ST, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Millear, A, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Mock, CN, Mohammad, KA, Mohammed, S, Mohanty, SK, Mokdad, AH, Mola, GLD, Molokhia, M, Monasta, L, Montico, M, Moradi-Lakeh, M, Moraga, P, Morawska, L, Mori, R, Moses, M, Mueller, UO, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naheed, A, Naldi, L, Nangia, V, Nascimento, BR, Negoi, I, Neupane, SP, Newton, CR, Ng, M, Ngalesoni, FN, Ngunjiri, JW, Nguyen, G, Ningrum, DNA, Nolte, S, Nomura, M, Norheim, OF, Norrving, B, Noubiap, JJN, Obermeyer, CM, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olivares, PR, Olsen, HE, Olusanya, BO, Olusanya, JO, Opio, JN, Oren, E, Ortiz, A, Osborne, RH, Osman, M, Owolabi, MO, PA, M, Pain, AW, Pakhale, S, Castillo, EP, Pana, A, Papachristou, C, Parsaeian, M, Patel, T, Patton, GC, Paudel, D, Paul, VK, Pearce, N, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pesudovs, K, Petzold, M, Phillips, MR, Pigott, DM, Pillay, JD, Pinho, C, Polinder, S, Pond, CD, Prakash, V, Purwar, M, Qorbani, M, Quistberg, DA, Radfar, A, Rafay, A, Rahimi, K, Rahimi-Movaghar, V, Rahman, M, Rahman, MHU, Rai, RK, Ram, U, Rana, SM, Rankin, Z, Rao, PV, Rao, PC, Rawaf, S, Rego, MAS, Reitsma, M, Remuzzi, G, Renzaho, AMNN, Resnikoff, S, Rezaei, S, Rezai, MS, Ribeiro, AL, Roba, HS, Rokni, MB, Ronfani, L, Roshandel, G, Roth, GA, Rothenbacher, D, Roy, NK, Sachdev, PS, Sackey, BB, Saeedi, MY, Safiri, S, Sagar, R, Sahraian, MA, Saleh, MM, Salomon, JA, Samy, AM, Sanabria, JR, Sanchez-Niño, MD, Sandar, L, Santos, IS, Santos, JV, Milicevic, MMS, Sarmiento-Suarez, R, Sartorius, B, Satpathy, M, Savic, M, Sawhney, M, Saylan, MI, Schöttker, B, Schutte, AE, Schwebel, DC, Seedat, S, Seid, AM, Seifu, CN, Sepanlou, SG, Serdar, B, Servan-Mori, EE, Setegn, T, Shackelford, KA, Shaheen, A, Shahraz, S, Shaikh, MA, Shakh-Nazarova, M, Shamsipour, M, Islam, SMS, Sharma, J, Sharma, R, She, J, Sheikhbahaei, S, Shen, J, Shi, P, Shigematsu, M, Shin, M-J, Shiri, R, Shoman, H, Shrime, MG, Sibamo, ELS, Sigfusdottir, ID, Silva, DAS, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, OP, Singh, PK, Singh, V, Sinke, AH, Sinshaw, AE, Skirbekk, V, Sliwa, K, Smith, A, Sobngwi, E, Soneji, S, Soriano, JB, Sousa, TCM, Sposato, LA, Sreeramareddy, CT, Stathopoulou, V, Steel, N, Steiner, C, Steinke, S, Stokes, MA, Stranges, S, Strong, M, Stroumpoulis, K, Sturua, L, Sufiyan, MB, Suliankatchi, RA, Sun, J, Sur, P, Swaminathan, S, Sykes, BL, Tabarés-Seisdedos, R, Tabb, KM, Taffere, GR, Talongwa, RT, Tarajia, M, Tavakkoli, M, Taveira, N, Teeple, S, Tegegne, TK, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Terkawi, AS, Tesema, AG, Thakur, JS, Thomson, AJ, Tillmann, T, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Tortajada, M, Troeger, C, Truelsen, T, Tura, AK, Uchendu, US, Ukwaja, KN, Undurraga, EA, Uneke, CJ, Uthman, OA, van Boven, JFM, Van Dingenen, R, Varughese, S, Vasankari, T, Venketasubramanian, N, Violante, FS, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Wagner, JA, Wakayo, T, Waller, SG, Walson, JL, Wang, H, Wang, Y-P, Watkins, DA, Weiderpass, E, Weintraub, RG, Wen, C-P, Werdecker, A, Wesana, J, Westerman, R, Whiteford, HA, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Won, S, Workicho, A, Workie, SB, Wubshet, M, Xavier, D, Xu, G, Yadav, AK, Yaghoubi, M, Yakob, B, Yan, LL, Yano, Y, Yaseri, M, Yimam, HH, Yip, P, Yonemoto, N, Yoon, S-J, Younis, MZ, Yu, C, Zaidi, Z, El Sayed Zaki, M, Zambrana-Torrelio, C, Zapata, T, Zenebe, ZM, Zodpey, S, Zoeckler, L, Zuhlke, LJ, Murray, CJL, Barber, RM, Fullman, N, Sorensen, RJD, Bollyky, T, McKee, M, Nolte, E, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abdulle, AM, Abdurahman, AA, Abera, SF, Abraham, B, Abreha, GF, Adane, K, Adelekan, AL, Adetifa, IMO, Afshin, A, Agarwal, A, Agarwal, SK, Agarwal, S, Agrawal, A, Kiadaliri, AA, Ahmadi, A, Ahmed, KY, Ahmed, MB, Akinyemi, RO, Akinyemiju, TF, Akseer, N, Al-Aly, Z, Alam, K, Alam, N, Alam, SS, Alemu, ZA, Alene, KA, Alexander, L, Ali, R, Ali, SD, Alizadeh-Navaei, R, Alkerwi, A, Alla, F, Allebeck, P, Allen, C, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Amini, E, Ammar, W, Amo-Adjei, J, Amoako, YA, Anderson, BO, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Ärnlöv, J, Artaman, A, Asayesh, H, Assadi, R, Astatkie, A, Atey, TM, Atique, S, Atnafu, NT, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Quintanilla, BPA, Awasthi, A, Ayele, NN, Azzopardi, P, Saleem, HOB, Bärnighausen, T, Bacha, U, Badawi, A, Banerjee, A, Barac, A, Barboza, MA, Barker-Collo, SL, Barrero, LH, Basu, S, Baune, BT, Baye, K, Bayou, YT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Béjot, Y, Bello, AK, Bennett, DA, Bensenor, IM, Berhane, A, Bernabé, E, Bernal, OA, Beyene, AS, Beyene, TJ, Bhutta, ZA, Biadgilign, S, Bikbov, B, Birlik, SM, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bose, D, Brainin, M, Brauer, M, Brazinova, A, Breitborde, NJK, Brenner, H, Butt, ZA, Cárdenas, R, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Carrero, JJ, Casey, D, Caso, V, Castañeda-Orjuela, CA, Rivas, JC, Catalá-López, F, Cecilio, P, Cercy, K, Charlson, FJ, Chen, AZ, Chew, A, Chibalabala, M, Chibueze, CE, Chisumpa, VH, Chitheer, AA, Chowdhury, R, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Coggeshall, MS, Cooper, LT, Cortinovis, M, Crump, JA, Dalal, K, Danawi, H, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, De Leo, D, Del Gobbo, LC, del Pozo-Cruz, B, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dicker, D, Ding, EL, Dokova, K, Dorsey, ER, Doyle, KE, Dubey, M, Ehrenkranz, R, Ellingsen, CL, Elyazar, I, Enayati, A, Ermakov, SP, Eshrati, B, Esteghamati, A, Estep, K, Fürst, T, Faghmous, IDA, Fanuel, FBB, Faraon, EJA, Farid, TA, Farinha, CSES, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Feigl, AB, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Fischer, F, Fitzmaurice, C, Fleming, TD, Foigt, N, Foreman, KJ, Forouzanfar, MH, Franklin, RC, Frostad, J, G/hiwot, TT, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gao, W, Garcia-Basteiro, AL, Gebre, T, Gebremedhin, AT, Gebremichael, MW, Gebru, AA, Gelaye, AA, Geleijnse, JM, Genova-Maleras, R, Gibney, KB, Giref, AZ, Gishu, MD, Giussani, G, Godwin, WW, Gold, A, Goldberg, EM, Gona, PN, Goodridge, A, Gopalani, SV, Goto, A, Graetz, N, Greaves, F, Griswold, M, Guban, PI, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Habtewold, TD, Hafezi-Nejad, N, Haile, D, Hailu, AD, Hailu, GB, Hakuzimana, A, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, Hendrie, D, Heredia-Pi, IB, Hoek, HW, Horino, M, Horita, N, Hosgood, HD, Htet, AS, Hu, G, Huang, H, Huang, JJ, Huntley, BM, Huynh, C, Iburg, KM, Ileanu, BV, Innos, K, Irenso, AA, Jahanmehr, N, Jakovljevic, MB, James, P, James, SL, Javanbakht, M, Jayaraman, SP, Jayatilleke, AU, Jeemon, P, Jha, V, John, D, Johnson, C, Johnson, SC, Jonas, JB, Juel, K, Kabir, Z, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Kasaeian, A, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kazanjan, K, Keiyoro, PN, Kemmer, L, Kemp, AH, Kengne, AP, Kerbo, AA, Kereselidze, M, Kesavachandran, CN, Khader, YS, Khalil, I, Khan, AR, Khan, EA, Khan, G, Khang, Y-H, Khoja, ATA, Khonelidze, I, Khubchandani, J, Kibret, GD, Kim, D, Kim, P, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kissoon, N, Kivipelto, M, Kokubo, Y, Kolk, A, Kolte, D, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krishnaswami, S, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulkarni, VS, Kumar, GA, Kumsa, FA, Kutz, M, Kyu, HH, Lager, ACJ, Lal, A, Lal, DK, Lalloo, R, Lallukka, T, Lan, Q, Langan, SM, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Latif, AA, Lawrynowicz, AEB, Leasher, JL, Leigh, J, Leinsalu, M, Leshargie, CT, Leung, J, Leung, R, Levi, M, Liang, X, Lim, SS, Lind, M, Linn, S, Lipshultz, SE, Liu, P, Liu, Y, Lo, L-T, Logroscino, G, Lopez, AD, Lorch, SA, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Macarayan, ERK, Mackay, MT, El Razek, HMA, El Razek, MMA, Mahdavi, M, Majeed, A, Malekzadeh, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marcenes, W, Marks, GB, Marquez, N, Martinez-Raga, J, Marzan, MB, Massano, J, Mathur, MR, Maulik, PK, Mazidi, M, McAlinden, C, McGrath, JJ, McNellan, C, Meaney, PA, Mehari, A, Mehndiratta, MM, Meier, T, Mekonnen, AB, Meles, KG, Memish, ZA, Mengesha, MM, Mengiste, DT, Mengistie, MA, Menota, BG, Mensah, GA, Mereta, ST, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Millear, A, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Mock, CN, Mohammad, KA, Mohammed, S, Mohanty, SK, Mokdad, AH, Mola, GLD, Molokhia, M, Monasta, L, Montico, M, Moradi-Lakeh, M, Moraga, P, Morawska, L, Mori, R, Moses, M, Mueller, UO, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naheed, A, Naldi, L, Nangia, V, Nascimento, BR, Negoi, I, Neupane, SP, Newton, CR, Ng, M, Ngalesoni, FN, Ngunjiri, JW, Nguyen, G, Ningrum, DNA, Nolte, S, Nomura, M, Norheim, OF, Norrving, B, Noubiap, JJN, Obermeyer, CM, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olivares, PR, Olsen, HE, Olusanya, BO, Olusanya, JO, Opio, JN, Oren, E, Ortiz, A, Osborne, RH, Osman, M, Owolabi, MO, PA, M, Pain, AW, Pakhale, S, Castillo, EP, Pana, A, Papachristou, C, Parsaeian, M, Patel, T, Patton, GC, Paudel, D, Paul, VK, Pearce, N, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pesudovs, K, Petzold, M, Phillips, MR, Pigott, DM, Pillay, JD, Pinho, C, Polinder, S, Pond, CD, Prakash, V, Purwar, M, Qorbani, M, Quistberg, DA, Radfar, A, Rafay, A, Rahimi, K, Rahimi-Movaghar, V, Rahman, M, Rahman, MHU, Rai, RK, Ram, U, Rana, SM, Rankin, Z, Rao, PV, Rao, PC, Rawaf, S, Rego, MAS, Reitsma, M, Remuzzi, G, Renzaho, AMNN, Resnikoff, S, Rezaei, S, Rezai, MS, Ribeiro, AL, Roba, HS, Rokni, MB, Ronfani, L, Roshandel, G, Roth, GA, Rothenbacher, D, Roy, NK, Sachdev, PS, Sackey, BB, Saeedi, MY, Safiri, S, Sagar, R, Sahraian, MA, Saleh, MM, Salomon, JA, Samy, AM, Sanabria, JR, Sanchez-Niño, MD, Sandar, L, Santos, IS, Santos, JV, Milicevic, MMS, Sarmiento-Suarez, R, Sartorius, B, Satpathy, M, Savic, M, Sawhney, M, Saylan, MI, Schöttker, B, Schutte, AE, Schwebel, DC, Seedat, S, Seid, AM, Seifu, CN, Sepanlou, SG, Serdar, B, Servan-Mori, EE, Setegn, T, Shackelford, KA, Shaheen, A, Shahraz, S, Shaikh, MA, Shakh-Nazarova, M, Shamsipour, M, Islam, SMS, Sharma, J, Sharma, R, She, J, Sheikhbahaei, S, Shen, J, Shi, P, Shigematsu, M, Shin, M-J, Shiri, R, Shoman, H, Shrime, MG, Sibamo, ELS, Sigfusdottir, ID, Silva, DAS, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, OP, Singh, PK, Singh, V, Sinke, AH, Sinshaw, AE, Skirbekk, V, Sliwa, K, Smith, A, Sobngwi, E, Soneji, S, Soriano, JB, Sousa, TCM, Sposato, LA, Sreeramareddy, CT, Stathopoulou, V, Steel, N, Steiner, C, Steinke, S, Stokes, MA, Stranges, S, Strong, M, Stroumpoulis, K, Sturua, L, Sufiyan, MB, Suliankatchi, RA, Sun, J, Sur, P, Swaminathan, S, Sykes, BL, Tabarés-Seisdedos, R, Tabb, KM, Taffere, GR, Talongwa, RT, Tarajia, M, Tavakkoli, M, Taveira, N, Teeple, S, Tegegne, TK, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Terkawi, AS, Tesema, AG, Thakur, JS, Thomson, AJ, Tillmann, T, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Tortajada, M, Troeger, C, Truelsen, T, Tura, AK, Uchendu, US, Ukwaja, KN, Undurraga, EA, Uneke, CJ, Uthman, OA, van Boven, JFM, Van Dingenen, R, Varughese, S, Vasankari, T, Venketasubramanian, N, Violante, FS, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Wagner, JA, Wakayo, T, Waller, SG, Walson, JL, Wang, H, Wang, Y-P, Watkins, DA, Weiderpass, E, Weintraub, RG, Wen, C-P, Werdecker, A, Wesana, J, Westerman, R, Whiteford, HA, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Won, S, Workicho, A, Workie, SB, Wubshet, M, Xavier, D, Xu, G, Yadav, AK, Yaghoubi, M, Yakob, B, Yan, LL, Yano, Y, Yaseri, M, Yimam, HH, Yip, P, Yonemoto, N, Yoon, S-J, Younis, MZ, Yu, C, Zaidi, Z, El Sayed Zaki, M, Zambrana-Torrelio, C, Zapata, T, Zenebe, ZM, Zodpey, S, Zoeckler, L, Zuhlke, LJ, and Murray, CJL

Published
2017

16. Global, regional, and national under-5 mortality, adult mortality, age-specific mortality, and life expectancy, 1970-2016: a systematic analysis for the Global Burden of Disease Study 2016

Author

Wang, H, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abera, SF, Abraha, HN, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adedoyin, RA, Adetifa, IMO, Adetokunboh, O, Afshin, A, Aggarwal, R, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akanda, S, Akinyemiju, TF, Akseer, N, Al-Eyadhy, A, Al Lami, FH, Alabed, S, Alahdab, F, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Aldridge, RW, Alene, KA, Alhabib, S, Ali, R, Alizadeh-Navaei, R, Aljunid, SM, Alkaabi, JM, Alkerwi, A, Alla, F, Allam, SD, Allebeck, P, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Ameh, EA, Amini, E, Ammar, W, Amoako, YA, Anber, N, Andrei, CL, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Anwari, P, Arnlov, J, Arora, M, Al, A, Aryal, KK, Asayesh, H, Asgedom, SW, Asghar, RJ, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Quintanilla, BPA, Babalola, TK, Bacha, U, Badawi, A, Balakrishnan, K, Balalla, S, Barac, A, Barber, RM, Barboza, MA, Barker-Collo, SL, Barnighausen, T, Barquera, S, Barregard, L, Barrero, LH, Baune, BT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Bejot, Y, Bekele, BB, Bell, ML, Bello, AK, Bennett, DA, Bennett, JR, Bensenor, IM, Benson, J, Berhane, A, Berhe, DF, Bernabe, E, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhaumik, S, Bhutta, ZA, Bikbov, B, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bjerregaard, P, Blosser, CD, Boneya, DJ, Boufous, S, Bourne, RRA, Brazinova, A, Breitborde, NJK, Brenner, H, Brugha, TS, Bukhman, G, Negesa, L, Bulto, B, Bumgarner, BR, Burch, M, Butt, ZA, Cahill, LE, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Car, M, Crdenas, R, Carpenter, DO, Carrero, JJ, Carter, A, Castaneda-Orjuela, CA, Rivas, JC, Castro, FF, Castro, RE, Catala-Lopez, F, Chen, H, Chiang, PP-C, Chibalabala, M, Chisumpa, VH, Chitheer, AA, Choi, J-YJ, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Cohen, AJ, Colquhoun, SM, Coresh, J, Criqui, MH, Cromwell, EA, Crump, JA, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, de Courten, B, De Leo, D, Degenhardt, L, Deiparine, S, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dherani, MK, Diaz-Torne, C, Ding, EL, Dixit, P, Djalalinia, S, Huyen, PD, Doku, DT, Donnelly, CA, Priscila, K, dos Santos, B, Douwes-Schultz, D, Driscoll, TR, Duan, L, Dubey, M, Duncan, BB, Dwivedi, LK, Ebrahimi, H, El Bcheraoui, C, Ellingsen, CL, Enayati, A, Endries, AY, Ermakov, SP, Eshetie, S, Eshrati, B, Eskandarieh, S, Esteghamati, A, Estep, K, Fanuel, BBF, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Filip, I, Fischer, F, Foigt, N, Foreman, KJ, Frank, T, Franklin, RC, Fraser, M, Friedman, J, Frostad, JJ, Fullman, N, Furst, T, Furtado, JM, Futran, ND, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gankpe, FG, Garcia-Basteiro, AL, Gebregergs, GB, Gebrehiwot, TT, Gebrekidan, KG, Gebremichael, MW, Gelaye, AA, Geleijnse, JM, Gemechu, BL, Gemechu, KS, Genova-Maleras, R, Gesesew, HA, Gething, PW, Gibney, KB, Gill, PS, Gillum, RF, Giref, AZ, Girma, BW, Giussani, G, Goenka, S, Gomez, B, Gona, PN, Gopalani, SV, Goulart, AC, Graetz, N, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Haagsma, JA, Hafezi-Nejad, N, Bidgoli, HH, Hakuzimana, A, Halasa, YA, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hancock, J, Handal, AJ, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Harikrishnan, S, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, He, F, Heredia-Pi, IB, Herteliu, C, Hilawe, EH, Hoek, HW, Horita, N, Hosgood, HD, Hostiuc, S, Hotez, PJ, Hoy, DG, Hsairi, M, Htet, AS, Hu, G, Huang, H, Huang, JJ, Iburg, KM, Igumbor, EU, Ileanu, BV, Inoue, M, Irenso, AA, Irvine, CMS, Islam, N, Jacobsen, KH, Jaenisch, T, Jahanmehr, N, Jakovljevic, MB, Javanbakht, M, Jayatilleke, AU, Jeemon, P, Jensen, PN, Jha, V, Jin, Y, John, D, John, O, Johnson, SC, Jonas, JB, Jurisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Karthikeyan, G, Kasaeian, A, Kassaw, NA, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kaul, A, Kawakami, N, Kazanjan, K, Keiyoro, PN, Kelbore, SG, Kemp, AH, Kengne, AP, Keren, A, Kereselidze, M, Kesavachandran, CN, Ketema, EB, Khader, YS, Khalil, IA, Khan, EA, Khan, G, Khang, Y-H, Khera, S, Khoja, ATA, Khosravi, MH, Kibret, GD, Kieling, C, Kim, C-I, Kim, D, Kim, P, Kim, S, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kishawi, S, Kissimova-Skarbek, KA, Kissoon, N, Kivimaki, M, Knudsen, AK, Kokubo, Y, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulikoff, XR, Kulkarni, VS, Kumar, GA, Kumar, P, Kumsa, FA, Kutz, M, Lachat, C, Lagat, AK, Lager, ACJ, Lal, DK, Lalloo, R, Lambert, N, Lan, Q, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Lavados, PM, Laxmaiah, A, Lee, PH, Leigh, J, Leung, J, Leung, R, Levi, M, Li, Y, Liao, Y, Liben, ML, Lim, SS, Linn, S, Lipshultz, SE, Liu, S, Lodha, R, Logroscino, G, Lorch, SA, Lorkowski, S, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Ma, S, Macarayan, ERK, Machado, IE, Mackay, MT, Abd el Razek, MM, Magis-Rodriguez, C, Mahdavi, M, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malhotra, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marczak, LB, Marks, GB, Martinez-Raga, J, Martins-Melo, FR, Massano, J, Maulik, PK, Mayosi, BM, Mazidi, M, McAlinden, C, McGarvey, ST, McGrath, JJ, Mckee, M, Mehata, S, Mehndiratta, MM, Mehta, KM, Meier, T, Mekonnen, TC, Meles, KG, Memiah, P, Memish, ZA, Mendoza, W, Mengesha, MM, Mengistie, MA, Tadese, D, Menon, MGR, Menota, BG, Mensah, GA, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Mikesell, J, Miller, TR, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Misganaw, A, Mishra, SR, Mohammad, KA, Mohammadi, A, Mohammed, KESM, Mohan, MBV, Mohanty, SK, Mokdad, AH, Assaye, AM, Mollenkopf, SK, Molokhia, M, Monasta, L, Hernandez, JCM, Montico, M, Mooney, MD, Moore, AR, Moradi-Lakeh, M, Moraga, P, Morawska, L, Velasquez, IM, Mori, R, Morrison, SD, Mruts, KB, Mueller, UO, Mullany, E, Muller, K, Venkata, G, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naidoo, KS, Nanda, L, Nangia, V, Nascimento, BR, Natarajan, G, Negoi, I, Cuong, TN, Ningrum, DNA, Nisar, MI, Nomura, M, Vuong, MN, Norheim, OF, Norrving, B, Noubiap, JJN, Nyakarahuka, L, Obermeyer, CM, O'Donnell, MJ, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olusanya, BO, Olusanya, JO, Oren, E, Ortiz, A, Osgood-Zimmerman, A, Ota, E, Owolabi, MO, Oyekale, AS, Pa, M, Pacella, RE, Pakhale, S, Pana, A, Panda, BK, Panda-Jonas, S, Park, E-K, Parsaeian, M, Patel, T, Patten, SB, Patton, GC, Paudel, D, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pervaiz, A, Pesudovs, K, Peterson, CB, Petri, WA, Petzold, M, Phillips, MR, Piel, FB, Pigott, DM, Pishgar, F, Plass, D, Polinder, S, Popova, S, Postma, MJ, Poulton, RG, Pourmalek, F, Prasad, N, Purwar, M, Qorbani, M, Rabiee, RHS, Radfar, A, Rafay, A, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, MHU, Rahman, SU, Rai, RK, Rajsic, S, Ram, U, Rana, SM, Ranabhat, CL, Rao, PV, Rawaf, S, Ray, SE, Rego, MAS, Rehm, J, Reiner, RC, Remuzzi, G, Renzaho, AMNN, Resnikoff, S, Rezaei, S, Rezai, MS, Ribeiro, AL, Rokni, MB, Ronfani, L, Roshandel, G, Roth, GA, Rothenbacher, D, Roy, A, Rubagotti, E, Ruhago, GM, Saadat, S, Sabde, YD, Sachdev, PS, Sadat, N, Safdarian, M, Safiri, SSS, Sagar, R, Sahathevan, R, Sahebkar, A, Sahraian, MA, Salama, J, Salamati, P, Salomon, JA, Salvi, SS, Samy, AM, Sanabria, JR, Sanchez-Nino, MD, Santos, IS, Milicevic, MMS, Sarmiento-Suarez, R, Sartorius, B, Satpathy, M, Sawhney, M, Saxena, S, Saylan, MI, Schmidt, MI, Schneider, IJC, Schutte, AE, Schwebel, DC, Schwendicke, F, Seedat, S, Seid, AM, Sepanlou, SG, Servan-Mori, EE, Shackelford, KA, Shaheen, A, Shahraz, S, Shaikh, MA, Shamsipour, M, Shamsizadeh, M, Islam, SMS, Sharma, J, Sharma, R, She, J, Shen, J, Shetty, BP, Shi, P, Shibuya, K, Shigematsu, M, Shiri, R, Shiue, I, Shrime, MG, Sigfusdottir, ID, Silberberg, DH, Silpakit, N, Silva, DAS, Silva, JP, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, PK, Singh, V, Sinha, DN, Skiadaresi, E, Sligar, A, Smith, DL, Sobaih, BHA, Sobngwi, E, Soneji, S, Soriano, JB, Sreeramareddy, CT, Srinivasan, V, Stathopoulou, V, Steel, N, Stein, DJ, Steiner, C, Stockl, H, Stokes, MA, Strong, M, Sufiyan, MB, Suliankatchi, RA, Sunguya, BF, Sur, PJ, Swaminathan, S, Sykes, BL, Szoeke, CEI, Tabares-Seisdedos, R, Tadakamadla, SK, Tadese, F, Tandon, N, Tanne, D, Tarajia, M, Tavakkoli, M, Taveira, N, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Temsah, M-H, Terkawi, AS, Tesema, CL, Tesssema, B, Theis, A, Thomas, N, Thompson, AH, Thomson, AJ, Thrift, AG, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Topouzis, F, Tortajada, M, Tran, BX, Trujillo, TTU, Tsilimparis, N, Tuem, KB, Tuzcu, EM, Tyrovolas, S, Ukwaja, KN, Undurraga, EA, Uthman, OA, Uzochukwu, BSC, van Boven, JFM, Varakin, YY, Varughese, S, Vasankari, T, Vasconcelos, AMN, Venketasubramanian, N, Vidavalur, R, Violante, FS, Vishnu, A, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Waid, JL, Wakayo, T, Wang, Y-P, Weichenthal, S, Weiderpass, E, Weintraub, RG, Werdecker, A, Wesana, J, Wijeratne, T, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Workicho, A, Workie, SB, Xavier, D, Xu, G, Yaghoubi, M, Yakob, B, Yalew, AZ, Yan, LL, Yano, Y, Yaseri, M, Ye, P, Yimam, HH, Yip, P, Yirsaw, BD, Yonemoto, N, Yoon, S-J, Yotebieng, M, Younis, MZ, Zaidi, Z, Zaki, MES, Zeeb, H, Zenebe, ZM, Zerfu, TA, Zhang, AL, Zhang, X, Zodpey, S, Zuhlke, LJ, Lopez, AD, Murray, CJL, Wang, H, Abajobir, AA, Abate, KH, Abbafati, C, Abbas, KM, Abd-Allah, F, Abera, SF, Abraha, HN, Abu-Raddad, LJ, Abu-Rmeileh, NME, Adedeji, IA, Adedoyin, RA, Adetifa, IMO, Adetokunboh, O, Afshin, A, Aggarwal, R, Agrawal, A, Agrawal, S, Kiadaliri, AA, Ahmed, MB, Aichour, AN, Aichour, I, Aichour, MTE, Aiyar, S, Akanda, S, Akinyemiju, TF, Akseer, N, Al-Eyadhy, A, Al Lami, FH, Alabed, S, Alahdab, F, Al-Aly, Z, Alam, K, Alam, N, Alasfoor, D, Aldridge, RW, Alene, KA, Alhabib, S, Ali, R, Alizadeh-Navaei, R, Aljunid, SM, Alkaabi, JM, Alkerwi, A, Alla, F, Allam, SD, Allebeck, P, Al-Raddadi, R, Alsharif, U, Altirkawi, KA, Martin, EA, Alvis-Guzman, N, Amare, AT, Ameh, EA, Amini, E, Ammar, W, Amoako, YA, Anber, N, Andrei, CL, Androudi, S, Ansari, H, Ansha, MG, Antonio, CAT, Anwari, P, Arnlov, J, Arora, M, Al, A, Aryal, KK, Asayesh, H, Asgedom, SW, Asghar, RJ, Assadi, R, Atey, TM, Atre, SR, Avila-Burgos, L, Avokpaho, EFGA, Awasthi, A, Quintanilla, BPA, Babalola, TK, Bacha, U, Badawi, A, Balakrishnan, K, Balalla, S, Barac, A, Barber, RM, Barboza, MA, Barker-Collo, SL, Barnighausen, T, Barquera, S, Barregard, L, Barrero, LH, Baune, BT, Bazargan-Hejazi, S, Bedi, N, Beghi, E, Bejot, Y, Bekele, BB, Bell, ML, Bello, AK, Bennett, DA, Bennett, JR, Bensenor, IM, Benson, J, Berhane, A, Berhe, DF, Bernabe, E, Beuran, M, Beyene, AS, Bhala, N, Bhansali, A, Bhaumik, S, Bhutta, ZA, Bikbov, B, Birungi, C, Biryukov, S, Bisanzio, D, Bizuayehu, HM, Bjerregaard, P, Blosser, CD, Boneya, DJ, Boufous, S, Bourne, RRA, Brazinova, A, Breitborde, NJK, Brenner, H, Brugha, TS, Bukhman, G, Negesa, L, Bulto, B, Bumgarner, BR, Burch, M, Butt, ZA, Cahill, LE, Cahuana-Hurtado, L, Campos-Nonato, IR, Car, J, Car, M, Crdenas, R, Carpenter, DO, Carrero, JJ, Carter, A, Castaneda-Orjuela, CA, Rivas, JC, Castro, FF, Castro, RE, Catala-Lopez, F, Chen, H, Chiang, PP-C, Chibalabala, M, Chisumpa, VH, Chitheer, AA, Choi, J-YJ, Christensen, H, Christopher, DJ, Ciobanu, LG, Cirillo, M, Cohen, AJ, Colquhoun, SM, Coresh, J, Criqui, MH, Cromwell, EA, Crump, JA, Dandona, L, Dandona, R, Dargan, PI, das Neves, J, Davey, G, Davitoiu, DV, Davletov, K, de Courten, B, De Leo, D, Degenhardt, L, Deiparine, S, Dellavalle, RP, Deribe, K, Deribew, A, Des Jarlais, DC, Dey, S, Dharmaratne, SD, Dherani, MK, Diaz-Torne, C, Ding, EL, Dixit, P, Djalalinia, S, Huyen, PD, Doku, DT, Donnelly, CA, Priscila, K, dos Santos, B, Douwes-Schultz, D, Driscoll, TR, Duan, L, Dubey, M, Duncan, BB, Dwivedi, LK, Ebrahimi, H, El Bcheraoui, C, Ellingsen, CL, Enayati, A, Endries, AY, Ermakov, SP, Eshetie, S, Eshrati, B, Eskandarieh, S, Esteghamati, A, Estep, K, Fanuel, BBF, Faro, A, Farvid, MS, Farzadfar, F, Feigin, VL, Fereshtehnejad, S-M, Fernandes, JG, Fernandes, JC, Feyissa, TR, Filip, I, Fischer, F, Foigt, N, Foreman, KJ, Frank, T, Franklin, RC, Fraser, M, Friedman, J, Frostad, JJ, Fullman, N, Furst, T, Furtado, JM, Futran, ND, Gakidou, E, Gambashidze, K, Gamkrelidze, A, Gankpe, FG, Garcia-Basteiro, AL, Gebregergs, GB, Gebrehiwot, TT, Gebrekidan, KG, Gebremichael, MW, Gelaye, AA, Geleijnse, JM, Gemechu, BL, Gemechu, KS, Genova-Maleras, R, Gesesew, HA, Gething, PW, Gibney, KB, Gill, PS, Gillum, RF, Giref, AZ, Girma, BW, Giussani, G, Goenka, S, Gomez, B, Gona, PN, Gopalani, SV, Goulart, AC, Graetz, N, Gugnani, HC, Gupta, PC, Gupta, R, Gupta, T, Gupta, V, Haagsma, JA, Hafezi-Nejad, N, Bidgoli, HH, Hakuzimana, A, Halasa, YA, Hamadeh, RR, Hambisa, MT, Hamidi, S, Hammami, M, Hancock, J, Handal, AJ, Hankey, GJ, Hao, Y, Harb, HL, Hareri, HA, Harikrishnan, S, Haro, JM, Hassanvand, MS, Havmoeller, R, Hay, RJ, Hay, SI, He, F, Heredia-Pi, IB, Herteliu, C, Hilawe, EH, Hoek, HW, Horita, N, Hosgood, HD, Hostiuc, S, Hotez, PJ, Hoy, DG, Hsairi, M, Htet, AS, Hu, G, Huang, H, Huang, JJ, Iburg, KM, Igumbor, EU, Ileanu, BV, Inoue, M, Irenso, AA, Irvine, CMS, Islam, N, Jacobsen, KH, Jaenisch, T, Jahanmehr, N, Jakovljevic, MB, Javanbakht, M, Jayatilleke, AU, Jeemon, P, Jensen, PN, Jha, V, Jin, Y, John, D, John, O, Johnson, SC, Jonas, JB, Jurisson, M, Kabir, Z, Kadel, R, Kahsay, A, Kalkonde, Y, Kamal, R, Kan, H, Karch, A, Karema, CK, Karimi, SM, Karthikeyan, G, Kasaeian, A, Kassaw, NA, Kassebaum, NJ, Kastor, A, Katikireddi, SV, Kaul, A, Kawakami, N, Kazanjan, K, Keiyoro, PN, Kelbore, SG, Kemp, AH, Kengne, AP, Keren, A, Kereselidze, M, Kesavachandran, CN, Ketema, EB, Khader, YS, Khalil, IA, Khan, EA, Khan, G, Khang, Y-H, Khera, S, Khoja, ATA, Khosravi, MH, Kibret, GD, Kieling, C, Kim, C-I, Kim, D, Kim, P, Kim, S, Kim, YJ, Kimokoti, RW, Kinfu, Y, Kishawi, S, Kissimova-Skarbek, KA, Kissoon, N, Kivimaki, M, Knudsen, AK, Kokubo, Y, Kopec, JA, Kosen, S, Koul, PA, Koyanagi, A, Kravchenko, M, Krohn, KJ, Defo, BK, Bicer, BK, Kuipers, EJ, Kulikoff, XR, Kulkarni, VS, Kumar, GA, Kumar, P, Kumsa, FA, Kutz, M, Lachat, C, Lagat, AK, Lager, ACJ, Lal, DK, Lalloo, R, Lambert, N, Lan, Q, Lansingh, VC, Larson, HJ, Larsson, A, Laryea, DO, Lavados, PM, Laxmaiah, A, Lee, PH, Leigh, J, Leung, J, Leung, R, Levi, M, Li, Y, Liao, Y, Liben, ML, Lim, SS, Linn, S, Lipshultz, SE, Liu, S, Lodha, R, Logroscino, G, Lorch, SA, Lorkowski, S, Lotufo, PA, Lozano, R, Lunevicius, R, Lyons, RA, Ma, S, Macarayan, ERK, Machado, IE, Mackay, MT, Abd el Razek, MM, Magis-Rodriguez, C, Mahdavi, M, Majdan, M, Majdzadeh, R, Majeed, A, Malekzadeh, R, Malhotra, R, Malta, DC, Mantovani, LG, Manyazewal, T, Mapoma, CC, Marczak, LB, Marks, GB, Martinez-Raga, J, Martins-Melo, FR, Massano, J, Maulik, PK, Mayosi, BM, Mazidi, M, McAlinden, C, McGarvey, ST, McGrath, JJ, Mckee, M, Mehata, S, Mehndiratta, MM, Mehta, KM, Meier, T, Mekonnen, TC, Meles, KG, Memiah, P, Memish, ZA, Mendoza, W, Mengesha, MM, Mengistie, MA, Tadese, D, Menon, MGR, Menota, BG, Mensah, GA, Meretoja, A, Meretoja, TJ, Mezgebe, HB, Micha, R, Mikesell, J, Miller, TR, Mills, EJ, Minnig, S, Mirarefin, M, Mirrakhimov, EM, Misganaw, A, Mishra, SR, Mohammad, KA, Mohammadi, A, Mohammed, KESM, Mohan, MBV, Mohanty, SK, Mokdad, AH, Assaye, AM, Mollenkopf, SK, Molokhia, M, Monasta, L, Hernandez, JCM, Montico, M, Mooney, MD, Moore, AR, Moradi-Lakeh, M, Moraga, P, Morawska, L, Velasquez, IM, Mori, R, Morrison, SD, Mruts, KB, Mueller, UO, Mullany, E, Muller, K, Venkata, G, Murthy, S, Musa, KI, Nachega, JB, Nagata, C, Nagel, G, Naghavi, M, Naidoo, KS, Nanda, L, Nangia, V, Nascimento, BR, Natarajan, G, Negoi, I, Cuong, TN, Ningrum, DNA, Nisar, MI, Nomura, M, Vuong, MN, Norheim, OF, Norrving, B, Noubiap, JJN, Nyakarahuka, L, Obermeyer, CM, O'Donnell, MJ, Ogbo, FA, Oh, I-H, Okoro, A, Oladimeji, O, Olagunju, AT, Olusanya, BO, Olusanya, JO, Oren, E, Ortiz, A, Osgood-Zimmerman, A, Ota, E, Owolabi, MO, Oyekale, AS, Pa, M, Pacella, RE, Pakhale, S, Pana, A, Panda, BK, Panda-Jonas, S, Park, E-K, Parsaeian, M, Patel, T, Patten, SB, Patton, GC, Paudel, D, Pereira, DM, Perez-Padilla, R, Perez-Ruiz, F, Perico, N, Pervaiz, A, Pesudovs, K, Peterson, CB, Petri, WA, Petzold, M, Phillips, MR, Piel, FB, Pigott, DM, Pishgar, F, Plass, D, Polinder, S, Popova, S, Postma, MJ, Poulton, RG, Pourmalek, F, Prasad, N, Purwar, M, Qorbani, M, Rabiee, RHS, Radfar, A, Rafay, A, Rahimi-Movaghar, A, Rahimi-Movaghar, V, Rahman, M, Rahman, MHU, Rahman, SU, Rai, RK, Rajsic, S, Ram, U, Rana, SM, Ranabhat, CL, Rao, PV, Rawaf, S, Ray, SE, Rego, MAS, Rehm, J, Reiner, RC, Remuzzi, G, Renzaho, AMNN, Resnikoff, S, Rezaei, S, Rezai, MS, Ribeiro, AL, Rokni, MB, Ronfani, L, Roshandel, G, Roth, GA, Rothenbacher, D, Roy, A, Rubagotti, E, Ruhago, GM, Saadat, S, Sabde, YD, Sachdev, PS, Sadat, N, Safdarian, M, Safiri, SSS, Sagar, R, Sahathevan, R, Sahebkar, A, Sahraian, MA, Salama, J, Salamati, P, Salomon, JA, Salvi, SS, Samy, AM, Sanabria, JR, Sanchez-Nino, MD, Santos, IS, Milicevic, MMS, Sarmiento-Suarez, R, Sartorius, B, Satpathy, M, Sawhney, M, Saxena, S, Saylan, MI, Schmidt, MI, Schneider, IJC, Schutte, AE, Schwebel, DC, Schwendicke, F, Seedat, S, Seid, AM, Sepanlou, SG, Servan-Mori, EE, Shackelford, KA, Shaheen, A, Shahraz, S, Shaikh, MA, Shamsipour, M, Shamsizadeh, M, Islam, SMS, Sharma, J, Sharma, R, She, J, Shen, J, Shetty, BP, Shi, P, Shibuya, K, Shigematsu, M, Shiri, R, Shiue, I, Shrime, MG, Sigfusdottir, ID, Silberberg, DH, Silpakit, N, Silva, DAS, Silva, JP, Silveira, DGA, Sindi, S, Singh, A, Singh, JA, Singh, PK, Singh, V, Sinha, DN, Skiadaresi, E, Sligar, A, Smith, DL, Sobaih, BHA, Sobngwi, E, Soneji, S, Soriano, JB, Sreeramareddy, CT, Srinivasan, V, Stathopoulou, V, Steel, N, Stein, DJ, Steiner, C, Stockl, H, Stokes, MA, Strong, M, Sufiyan, MB, Suliankatchi, RA, Sunguya, BF, Sur, PJ, Swaminathan, S, Sykes, BL, Szoeke, CEI, Tabares-Seisdedos, R, Tadakamadla, SK, Tadese, F, Tandon, N, Tanne, D, Tarajia, M, Tavakkoli, M, Taveira, N, Tehrani-Banihashemi, A, Tekelab, T, Tekle, DY, Shifa, GT, Temsah, M-H, Terkawi, AS, Tesema, CL, Tesssema, B, Theis, A, Thomas, N, Thompson, AH, Thomson, AJ, Thrift, AG, Tiruye, TY, Tobe-Gai, R, Tonelli, M, Topor-Madry, R, Topouzis, F, Tortajada, M, Tran, BX, Trujillo, TTU, Tsilimparis, N, Tuem, KB, Tuzcu, EM, Tyrovolas, S, Ukwaja, KN, Undurraga, EA, Uthman, OA, Uzochukwu, BSC, van Boven, JFM, Varakin, YY, Varughese, S, Vasankari, T, Vasconcelos, AMN, Venketasubramanian, N, Vidavalur, R, Violante, FS, Vishnu, A, Vladimirov, SK, Vlassov, VV, Vollset, SE, Vos, T, Waid, JL, Wakayo, T, Wang, Y-P, Weichenthal, S, Weiderpass, E, Weintraub, RG, Werdecker, A, Wesana, J, Wijeratne, T, Wilkinson, JD, Wiysonge, CS, Woldeyes, BG, Wolfe, CDA, Workicho, A, Workie, SB, Xavier, D, Xu, G, Yaghoubi, M, Yakob, B, Yalew, AZ, Yan, LL, Yano, Y, Yaseri, M, Ye, P, Yimam, HH, Yip, P, Yirsaw, BD, Yonemoto, N, Yoon, S-J, Yotebieng, M, Younis, MZ, Zaidi, Z, Zaki, MES, Zeeb, H, Zenebe, ZM, Zerfu, TA, Zhang, AL, Zhang, X, Zodpey, S, Zuhlke, LJ, Lopez, AD, and Murray, CJL

Published
2017

17. A classification algorithm for predicting progression from normal cognition to mild cognitive impairment across five cohorts: The preclinical AD consortium.

Author

Gross, AL, Hassenstab, JJ, Johnson, SC, Clark, LR, Resnick, SM, Kitner-Triolo, M, Masters, CL, Maruff, P, Morris, JC, Soldan, A, Pettigrew, C, Albert, MS, Gross, AL, Hassenstab, JJ, Johnson, SC, Clark, LR, Resnick, SM, Kitner-Triolo, M, Masters, CL, Maruff, P, Morris, JC, Soldan, A, Pettigrew, C, and Albert, MS

Abstract

INTRODUCTION: We established a method for diagnostic harmonization across multiple studies of preclinical Alzheimer's disease and validated the method by examining its relationship with clinical status and cognition. METHODS: Cognitive and clinical data were used from five studies (N = 1746). Consensus diagnoses established in each study used criteria to identify progressors from normal cognition to mild cognitive impairment. Correspondence was evaluated between these consensus diagnoses and three algorithmic classifications based on (1) objective cognitive impairment in 2+ tests only; (2) a Clinical Dementia Rating (CDR) of ≥0.5 only; and (3) both. Associations between baseline cognitive performance and cognitive change were each tested in relation to progression to algorithm-based classifications. RESULTS: In each study, an algorithmic classification based on both cognitive testing cutoff scores and a CDR ≥0.5 provided optimal balance of sensitivity and specificity (areas under the curve: 0.85-0.95). Over an average 6.6 years of follow-up (up to 28 years), N = 186 initially cognitively normal participants aged on average 64 years at baseline progressed (incidence rate: 15.3 people/1000 person-years). Baseline cognitive scores and cognitive change were associated with future diagnostic status using this algorithmic classification. DISCUSSION: Both cognitive tests and CDR ratings can be combined across multiple studies to obtain a reliable algorithmic classification with high specificity and sensitivity. This approach may be applicable to large cohort studies and to clinical trials focused on preclinical Alzheimer's disease because it provides an alternative to implementation of a time-consuming adjudication panel.

Published
2017

20. Is VO2 max an appropriate fitness indicator for older adults?

Author

White AT, Fehlauer CS, Hanover R, Johnson SC, and Dustman RE

Abstract

Older individuals are more likely than younger adults to exhibit symptoms of exercise intolerance at high work rates. The risks of maximal exercise in older adults increase proportionally as the number of health difficulties increase. In this study, the effects of health status, age, and gender on older adults' ability to attain VO2 max are examined. Sedentary volunteers (60 women, 45 men), mean age 67 +/- 5 years (range 57-78 years), participated in graded maximal exercise tests on a combined arm and leg cycle ergometer. Subjects were classified into three groups based on test termination reason: attainment of VO2 max (MAX), symptom-limited (SX), or EKG-limited (EKG). Sixty percent of men and 40% of women were classified as MAX, while 48% of women and 27% of men were characterized as SX. Thirteen percent of men and 12% of women had EKG-limited exercise tests. Those in the EKG group reported significantly more diagnoses than subjects in the MAX group (2.7 vs. 1.4, p < .05). The number of medications reported and age of the subjects did not differ across test termination categories. [ABSTRACT FROM AUTHOR]

Published
1998
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22. THE RING OF CONFIDENCE

Author

Johnson, SC

Subjects
Business, Business, international, Food and beverage industries
Abstract

Sir; Your otherwise excellent news review of 2000 (The Grocer, December 23) paid scant regard to one of the greatest achievements of last year -- formation of Britain's first independent [...]

Published
2001

38. Insulin resistance, brain atrophy, and cognitive performance in late middle-aged adults.

Author

Willette AA, Xu G, Johnson SC, Birdsill AC, Jonaitis EM, Sager MA, Hermann BP, La Rue A, Asthana S, Bendlin BB, Willette, Auriel A, Xu, Guofan, Johnson, Sterling C, Birdsill, Alex C, Jonaitis, Erin M, Sager, Mark A, Hermann, Bruce P, La Rue, Asenath, Asthana, Sanjay, and Bendlin, Barbara B

Subjects
BRAIN diseases, BRAIN mapping, COGNITION, INSULIN resistance, NEUROPSYCHOLOGICAL tests, RESEARCH funding, ATROPHY, GENOTYPES
Abstract

Objective: Insulin resistance dysregulates glucose uptake and other functions in brain areas affected by Alzheimer disease. Insulin resistance may play a role in Alzheimer disease etiopathogenesis. This longitudinal study examined whether insulin resistance among late middle-aged, cognitively healthy individuals was associated with 1) less gray matter in Alzheimer disease-sensitive brain regions and 2) worse cognitive performance.Research Design and Methods: Homeostasis model assessment of insulin resistance, gray matter volume, and the Rey Auditory Verbal Learning Test (RAVLT) were acquired in 372 participants at baseline and a consecutive subset of 121 individuals ~4 years later. Voxel-based morphometry and tensor-based morphometry were used, respectively, to test the association of insulin resistance with baseline brain volume and progressive gray matter atrophy.Results: Higher insulin resistance predicted less gray matter at baseline and 4 years later in medial temporal lobe, prefrontal cortices, precuneus, and other parietal gyri. A region-of-interest analysis, independent of the voxel-wise analyses, confirmed that higher insulin resistance was related to medial temporal lobe atrophy. Atrophy itself corresponded to cognitive deficits in the RAVLT. Temporal lobe atrophy that was predicted by higher insulin resistance significantly mediated worse RAVLT encoding performance.Conclusions: These results suggest that insulin resistance in an asymptomatic, late middle-aged cohort is associated with progressive atrophy in regions affected by early Alzheimer disease. Insulin resistance may also affect the ability to encode episodic information by negatively influencing gray matter volume in medial temporal lobe. [ABSTRACT FROM AUTHOR]

Published
2013
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44. Financial well-being and postdeployment adjustment among Iraq and Afghanistan war veterans.

Author

Elbogen EB, Johnson SC, Wagner HR, Newton VM, Beckham JC, Elbogen, Eric B, Johnson, Sally C, Wagner, H Ryan, Newton, Virginia M, and Beckham, Jean C

Abstract

Research has yet to examine the relationship between financial well-being and community reintegration of veterans. To address this, we analyzed data from n = 1,388 Iraq and Afghanistan War Era Veterans who completed a national survey on postdeployment adjustment. The results indicated that probable major depressive disorder, posttraumatic stress disorder, and traumatic brain injury were associated with financial difficulties. However, regardless of diagnosis, veterans who reported having money to cover basic needs were significantly less likely to have postdeployment adjustment problems such as criminal arrest, homelessness, substance abuse, suicidal behavior, and aggression. Statistical analyses also indicated that poor money management (e.g., incurring significant debt or writing bad checks) was related to maladjustment, even among veterans at higher income levels. Given these findings, efforts aimed at enhancing financial literacy and promoting meaningful employment may have promise to enhance outcomes and improve quality of life among returning veterans. [ABSTRACT FROM AUTHOR]

Published
2012
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46. Impulsivity is not always associated with student drinking: A moderation study of impulsivity and drinking by positive alcohol expectancies.

Author

Carlson SR and Johnson SC

Abstract

Student drinking is a major problem on North American campuses and impulsivity is a significant risk factor for heavy drinking. The present study investigates the moderation of the impulsivity-drinking relationship by the expectation that having a drink will lead to positive experiences. Undergraduate drinkers (n=292) completed measures of impulsivity (Barratt Impulsiveness Scale 11; BIS-11), positive drinking expectancies, and alcohol use. Expectancies moderated the relationship between BIS-11 scores and alcohol use. BIS-11 scores were significantly related to typical alcohol quantity, frequency, quantity X frequency, and binge drinking frequency for individuals with average and high levels of positive expectancies, but not for those with few positive expectancies. Implications for interventions targeted at highly impulsive students, using expectancy modification are discussed. [ABSTRACT FROM AUTHOR]

Published
2012

47. The effects of ramipril in individuals at risk for Alzheimer's disease: results of a pilot clinical trial.

Author

Wharton W, Stein JH, Korcarz C, Sachs J, Olson SR, Zetterberg H, Dowling M, Ye S, Gleason CE, Underbakke G, Jacobson LE, Johnson SC, Sager MA, Asthana S, Carlsson CM, Wharton, Whitney, Stein, James H, Korcarz, Claudia, Sachs, Jane, and Olson, Sandra R

Abstract

Research shows that certain antihypertensives taken during midlife confer Alzheimer's disease (AD) related benefits in later life. We conducted a clinical trial to evaluate the extent to which the angiotensin converting enzyme inhibitor (ACE-I), ramipril, affects AD biomarkers including cerebrospinal fluid (CSF) amyloid-β (Aβ) levels and ACE activity, arterial function, and cognition in participants with a parental history of AD. This four month randomized, double-blind, placebo-controlled, pilot clinical trial evaluated the effects of ramipril, a blood-brain-barrier crossing ACE-I, in cognitively healthy individuals with mild, or Stage I hypertension. Fourteen participants were stratified by gender and apolipoprotein E ε4 (APOE ε4) status and randomized to receive 5 mg of ramipril or matching placebo daily. Participants were assessed at baseline and month 4 on measures of CSF Aβ(1-42) and ACE activity, arterial function, and cognition. Participants were middle-aged (mean 54 y) and highly educated (mean 15.4 y), and included 50% men and 50% APOE ε4 carriers. While results did not show a treatment effect on CSF Aβ(1-42) (p = 0.836), data revealed that ramipril can inhibit CSF ACE activity (p = 0.009) and improve blood pressure, however, there were no differences between groups in arterial function or cognition. In this study, ramipril therapy inhibited CSF ACE activity and improved blood pressure, but did not influence CSF Aβ1-42. While larger trials are needed to confirm our CSF Aβ results, it is possible that prior research reporting benefits of ACE-I during midlife may be attributed to alternative mechanisms including improvements in cerebral blood flow or the prevention of angiotensin II-mediated inhibition of acetylcholine. [ABSTRACT FROM AUTHOR]

Published
2012

48. A within-subject comparison of 6-[18F]fluoro-m-tyrosine and 6-[18F]fluoro-L-dopa in Parkinson's disease.

Author

Gallagher CL, Christian BT, Holden JE, Dejesus OT, Nickles RJ, Buyan-Dent L, Bendlin BB, Harding SJ, Stone CK, Mueller B, Johnson SC, Gallagher, Catherine L, Christian, Bradley T, Holden, James E, Dejesus, Onofre T, Nickles, Robert J, Buyan-Dent, Laura, Bendlin, Barbara B, Harding, Sandra J, and Stone, Charles K

Subjects
DOPA, BRAIN, FLUORINE isotopes, PARKINSON'S disease, RADIOISOTOPES, RESEARCH funding, POSITRON emission tomography, TYROSINE, SEVERITY of illness index
Abstract

Progression of Parkinson's disease symptoms is imperfectly correlated with positron emission tomography biomarkers for dopamine biosynthetic pathways. The radiopharmaceutical 6-[(18) F]fluoro-m-tyrosine is not a substrate for catechol-O-methyltransferase and therefore has a more favorable uptake-to-background ratio than 6-[(18) F]fluoro-L-dopa. The objective of this study was to evaluate 6-[(18) F]fluoro-m-tyrosine relative to 6-[(18) F]fluoro-L-dopa with partial catechol-O-methyltransferase inhibition as a biomarker for clinical status in Parkinson's disease. Twelve patients with early-stage Parkinson's disease, off medication, underwent Unified Parkinson Disease Rating Scale scoring, brain magnetic resonance imaging, and 3-dimensional dynamic positron emission tomography using equivalent doses of 6-[(18) F]fluoro-m-tyrosine and 6-[(18) F]fluoro-L-dopa with tolcapone, a catechol-O-methyltransferase inhibitor. Images were realigned within subject, after which the tissue-derived uptake rate constant was generated for volumes of interest encompassing the caudate nucleus, putamen, and subregions of the putamen. We computed both bivariate (Pearson) and partial (covariate of age) correlations between clinical subscores and tissue-derived uptake rate constant. Tissue-derived uptake rate constant values were correlated between the radiopharmaceuticals (r = 0.8). Motor subscores were inversely correlated with the contralateral putamen 6-[(18) F]fluoro-m-tyrosine tissue-derived uptake rate constant (|r| > 0.72, P < .005) but not significantly with the 6-[(18) F]fluoro-L-dopa tissue-derived uptake rate constant. The uptake rate constants for both radiopharmaceuticals were also inversely correlated with activities of daily living subscores, but the magnitude of correlation coefficients was greater for 6-[(18) F]fluoro-m-tyrosine. In this design, 6-[(18) F]fluoro-m-tyrosine uptake better reflected clinical status than did 6-[(18) F]fluoro-L-dopa uptake. We attribute this finding to 6-[(18) F]fluoro-m-tyrosine's higher affinity for the target, L-aromatic amino acid decarboxylase, and the absence of other major determinants of the uptake rate constant. These results also imply that L-aromatic amino acid decarboxylase activity is a major determinant of clinical status. [ABSTRACT FROM AUTHOR]

Published
2011
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49. Swallowing intentional off-state in aging and Alzheimer's disease: preliminary study.

Author

Humbert IA, McLaren DG, Malandraki G, Johnson SC, Robbins J, Humbert, Ianessa A, McLaren, Donald G, Malandraki, Georgia, Johnson, Sterling C, and Robbins, Joanne

Subjects
AGING, ALZHEIMER'S disease, BRAIN mapping, CEREBRAL cortex, DEGLUTITION, DIGITAL image processing, MAGNETIC resonance imaging, RESEARCH funding
Abstract

Frontal cortical activation is elicited when subjects have been instructed not to initiate a sensorimotor task. The goal of this preliminary fMRI study was to examine BOLD response to a "Do Not Swallow" instruction (an intentional "off-state") in the context of other swallowing tasks in 3 groups of participants (healthy young, healthy old, and early Alzheimer's disease (AD)). Overall, the older group had larger, bilaterally active clusters in the cortex, including the dorsomedial prefrontal cortex during the intentional swallowing off-state; this region is commonly active in response inhibition studies. Disease-related differences were evident where the AD group had significantly greater BOLD response in the insula/operculum than the old. These findings have significant clinical implications for control of swallowing across the age span and in neurodegenerative disease. Greater activation in the insula/operculum for the AD group supports previous studies where this region is associated with initiating swallowing. The AD group may have required more effort to "turn off" swallowing centers to reach the intentional swallowing off-state. [ABSTRACT FROM AUTHOR]

Published
2011
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50. Domestic violence and forced sex among the urban poor in South India: implications for HIV prevention.

Author

Solomon S, Subbaraman R, Solomon SS, Srikrishnan AK, Johnson SC, Vasudevan CK, Anand S, Ganesh AK, and Celentano DD

Abstract

This article examined the prevalence of physical and sexual violence among 1,974 married women from 40 low-income communities in Chennai, India. The authors found a 99% and 75% lifetime prevalence of physical abuse and forced sex, respectively, whereas 65% of women experienced more than five episodes of physical abuse in the 3 months preceding the survey. Factors associated with violence after multivariate adjustment included elementary/middle school education and variables suggesting economic insecurity. These domestic violence rates exceed those in prior Indian reports, suggesting women in slums may be at increased risk for HIV and other sexually transmitted infections. [ABSTRACT FROM AUTHOR]

Published
2009
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