Chandra L Jackson

Dr. Chandra L Jackson

orcid

Research Focus & Metrics

Academic Career Length

22Years (2005-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 August 2016 in The Lancet
Verified
Journal Article
Open access
EAEmanuele Di Angelantonio
Emanuele Di Angelantonio
SBShilpa N Bhupathiraju
Shilpa N Bhupathiraju
DWDavid Wormser
David Wormser
PGPei Gao
Pei Gao
SKStephen Kaptoge
Stephen Kaptoge
FHFrank B Hu
Frank B Hu
Overweight and obesity are increasing worldwide. To help assess their relevance to mortality in different populations we conducted individual-participant data meta-analyses of prospective studies of body-mass index (BMI), limiting confounding and reverse causality by restricting analyses to never-smokers and excluding pre-existing disease and the first 5 years of follow-up. Of 10 625 411 participants in Asia, Australia and New Zealand, Europe, and North America from 239 prospective studies (median follow-up 13·7 years, IQR 11·4-14·7), 3 951 455 people in 189 studies were never-smokers without chronic diseases at recruitment who survived 5 years, of whom 385 879 died. The primary analyses are of these deaths, and study, age, and sex adjusted hazard ratios (HRs), relative to BMI 22·5-<25·0 kg/m(2). All-cause mortality was minimal at 20·0-25·0 kg/m(2) (HR 1·00, 95% CI 0·98-1·02 for BMI 20·0-<22·5 kg/m(2); 1·00, 0·99-1·01 for BMI 22·5-<25·0 kg/m(2)), and increased significantly both just below this range (1·13, 1·09-1·17 for BMI 18·5-<20·0 kg/m(2); 1·51, 1·43-1·59 for BMI 15·0-<18·5) and throughout the overweight range (1·07, 1·07-1·08 for BMI 25·0-<27·5 kg/m(2); 1·20, 1·18-1·22 for BMI 27·5-<30·0 kg/m(2)). The HR for obesity grade 1 (BMI 30·0-<35·0 kg/m(2)) was 1·45, 95% CI 1·41-1·48; the HR for obesity grade 2 (35·0-<40·0 kg/m(2)) was 1·94, 1·87-2·01; and the HR for obesity grade 3 (40·0-<60·0 kg/m(2)) was 2·76, 2·60-2·92. For BMI over 25·0 kg/m(2), mortality increased approximately log-linearly with BMI; the HR per 5 kg/m(2) units higher BMI was 1·39 (1·34-1·43) in Europe, 1·29 (1·26-1·32) in North America, 1·39 (1·34-1·44) in east Asia, and 1·31 (1·27-1·35) in Australia and New Zealand. This HR per 5 kg/m(2) units higher BMI (for BMI over 25 kg/m(2)) was greater in younger than older people (1·52, 95% CI 1·47-1·56, for BMI measured at 35-49 years vs 1·21, 1·17-1·25, for BMI measured at 70-89 years; pheterogeneity<0·0001), greater in men than women (1·51, 1·46-1·56, vs 1·30, 1·26-1·33; pheterogeneity<0·0001), but similar in studies with self-reported and measured BMI. The associations of both overweight and obesity with higher all-cause mortality were broadly consistent in four continents. This finding supports strategies to combat the entire spectrum of excess adiposity in many populations. UK Medical Research Council, British Heart Foundation, National Institute for Health Research, US National Institutes of Health.

12 February 2015 in Diabetes Care
Verified
Journal Article
Closed access
PDF file
ZSZhilei Shan
Zhilei Shan
HMHongfei Ma
Hongfei Ma
MXManling Xie
Manling Xie
PYPeipei Yan
Peipei Yan
YGYanjun Guo
Yanjun Guo
LLLiegang Liu
Liegang Liu
OBJECTIVE: It remains unclear how many hours of sleep are associated with the lowest risk of type 2 diabetes. This meta-analysis was performed to assess the dose-response relationship between sleep duration and risk of type 2 diabetes. RESEARCH DESIGN AND METHODS: PubMed and Embase were searched up to 20 March 2014 for prospective observational studies that assessed the relationship of sleep duration and risk of type 2 diabetes. Both semiparametric and parametric methods were used. RESULTS: Ten articles with 11 reports were eligible for inclusion in the meta-analysis. A total of 18,443 incident cases of type 2 diabetes were ascertained among 482,502 participants with follow-up periods ranging from 2.5 to 16 years. A U-shaped dose-response relationship was observed between sleep duration and risk of type 2 diabetes, with the lowest risk observed at a sleep duration category of 7–8 h per day. Compared with 7-h sleep duration per day, the pooled relative risks for type 2 diabetes were 1.09 (95% CI 1.04–1.15) for each 1-h shorter sleep duration among individuals who slept <7 h per day and 1.14 (1.03–1.26) for each 1-h increment of sleep duration among individuals with longer sleep duration. CONCLUSIONS: Our dose-response meta-analysis of prospective studies shows a U-shaped relationship between sleep duration and risk of type 2 diabetes, with the lowest type 2 diabetes risk at 7–8 h per day of sleep duration. Both short and long sleep duration are associated with a significantly increased risk of type 2 diabetes, underscoring the importance of appropriate sleep duration in the delay or prevention of type 2 diabetes.

1 June 2015 in SLEEP
Verified
Journal Article
Closed access
XCXiaoli Chen
Xiaoli Chen
RWRui Wang
Rui Wang
PZPhyllis Zee
Phyllis Zee
PLPamela L. Lutsey
Pamela L. Lutsey
SJSogol Javaheri
Sogol Javaheri
SRSusan Redline
Susan Redline
S and full research articles published in SLEEP, the official journal of the APSS. Sleep and sleep disorder scientific findings.

16 January 2014 in New England Journal of Medicine
Verified
Journal Article
Closed access
PDF file
DTDeirdre K. Tobias
Deirdre K. Tobias
APAn Pan
An Pan
CJChandra L Jackson
Chandra L Jackson
EOEilis J. O'Reilly
Eilis J. O'Reilly
EDEric L. Ding
Eric L. Ding
FHFrank B. Hu
Frank B. Hu
The relation between body weight and mortality among persons with type 2 diabetes remains unresolved, with some studies suggesting decreased mortality among overweight or obese persons as compared with normal-weight persons (an “obesity paradox”). We studied participants with incident diabetes from the Nurses' Health Study (8970 participants) and Health Professionals Follow-up Study (2457 participants) who were free of cardiovascular disease and cancer at the time of a diagnosis of diabetes. Body weight shortly before diagnosis and height were used to calculate the body-mass index (BMI, the weight in kilograms divided by the square of the height in meters). Multivariable Cox models were used to estimate the hazard ratios and 95% confidence intervals for mortality across BMI categories. There were 3083 deaths during a mean period of 15.8 years of follow-up. A J-shaped association was observed across BMI categories (18.5 to 22.4, 22.5 to 24.9 [reference], 25.0 to 27.4, 27.5 to 29.9, 30.0 to 34.9, and ≥35.0) for all-cause mortality (hazard ratio, 1.29 [95% confidence interval {CI}, 1.05 to 1.59]; 1.00; 1.12 [95% CI, 0.98 to 1.29]; 1.09 [95% CI, 0.94 to 1.26]; 1.24 [95% CI, 1.08 to 1.42]; and 1.33 [95% CI, 1.14 to 1.55], respectively). This relationship was linear among participants who had never smoked (hazard ratios across BMI categories: 1.12, 1.00, 1.16, 1.21, 1.36, and 1.56, respectively) but was nonlinear among participants who had ever smoked (hazard ratios across BMI categories: 1.32, 1.00, 1.09, 1.04, 1.14, and 1.21) (P=0.04 for interaction). A direct linear trend was observed among participants younger than 65 years of age at the time of a diabetes diagnosis but not among those 65 years of age or older at the time of diagnosis (P<0.001 for interaction). We observed a J-shaped association between BMI and mortality among all participants and among those who had ever smoked and a direct linear relationship among those who had never smoked. We found no evidence of lower mortality among patients with diabetes who were overweight or obese at diagnosis, as compared with their normal-weight counterparts, or of an obesity paradox. (Funded by the National Institutes of Health and the American Diabetes Association.)

28 September 2023 in The Lancet Public Health
Verified
Journal Article
Open access
DLDiane C Lim
Diane C Lim
ANArezu Najafi
Arezu Najafi
LALamia Afifi
Lamia Afifi
CBClaudio LA Bassetti
Claudio LA Bassetti
DBDaniel J Buysse
Daniel J Buysse
CJChandra L Jackson
Chandra L Jackson
Healthy sleep is essential for physical and mental health, and social wellbeing; however, across the globe, and particularly in developing countries, national public health agendas rarely consider sleep health. Sleep should be promoted as an essential pillar of health, equivalent to nutrition and physical activity. To improve sleep health across the globe, a focus on education and awareness, research, and targeted public health policies are needed. We recommend developing sleep health educational programmes and awareness campaigns; increasing, standardising, and centralising data on sleep quantity and quality in every country across the globe; and developing and implementing sleep health policies across sectors of society. Efforts are needed to ensure equity and inclusivity for all people, particularly those who are most socially and economically vulnerable, and historically excluded.

1 July 2019 in Nature and Science of Sleep
Verified
Journal Article
Open access
PDF file
DJDayna A. Johnson
Dayna A. Johnson
CJChandra L Jackson
Chandra L Jackson
NWNatasha J Williams
Natasha J Williams
CACarmela Alcántara
Carmela Alcántara
Sleep is a fundamental necessity of life. However, sleep health and sleep disorders are not equitably distributed across racial/ethnic groups. In fact, growing research consistently demonstrates that racial/ethnic minorities are more likely to experience, for instance, shorter sleep durations, less deep sleep, inconsistent sleep timing, and lower sleep continuity in comparison to Whites. However, racial/ethnic disparities in reports of sleepiness and sleep complaints are inconsistent. Racial/ethnic groups have significant heterogeneity, yet within-group analyses are limited. Among the few published within-group analyses, there are differences in sleep between non-US-born and US-born racial/ethnic groups, but the group with the more favorable sleep profile is consistent for non-US-born Latinos compared to US-born Latinos and Whites but unclear for other racial/ethnic minority groups. These sleep health disparities are a significant public health problem that should garner support for more observational, experimental, intervention, and policy/implementation research. In this review, we 1) summarize current evidence related to racial/ethnic disparities in sleep health and within-group differences, focusing on the sleep of the following racial/ethnic minority categories that are defined by the United States Office of Management and Budget as: American Indian/Alaska Native, Asian, African American/Black, Hispanic/Latino, and Native Hawaiian/Pacific Islander; 2) discuss measurement challenges related to investigating sleep health disparities; 3) discuss potential contributors to sleep health disparities; 4) present promising interventions to address sleep health disparities; and 5) discuss future research directions on intersectionality and sleep health.