scholarly journals Impact of cardiovascular health status on the dose-response relationship between physical activity and incident morbidity and mortality

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
E.A Bakker ◽  
E.J Oymans ◽  
M.T.E Hopman ◽  
D.H.J Thijssen ◽  
T.M.H Eijsvogels
Keyword(s):  
Health Status ◽  
The Netherlands ◽  
Dose Response ◽  
Cardiovascular Health ◽  
Adverse Outcomes ◽  
Response Relationship ◽  
Healthy Individuals ◽  
Dose Response Relationship ◽  
Active Lifestyle ◽  
Physically Active

Abstract Background Although the health benefits of a physically active lifestyle are well-known, there is ongoing discussion about the dose-response relationship between physical activity (PA) and incident morbidity/mortality, and whether this association may be affected by cardiovascular health status. Purpose We compared the dose-response relationship of PA, incident major cardiovascular events (MACE) and all-cause mortality between healthy individuals and individuals with cardiovascular disease risk factors (CVRF) or cardiovascular diseases (CVD). Methods This study used data from Lifelines, which is a multi-disciplinary population-based cohort including 167,729 participants from the northern population of the Netherlands. Adults (>18 yrs old) without severe illnesses or limited life expectancy (<5 yrs) were included (N=143,059). PA volumes were presented as Metabolic Equivalent of Task (MET) minutes/week, and divided into quartiles (Q1-Q4). The primary outcome was a composite endpoint of incident MACE (i.e. myocardial infarction, stroke, heart failure, CABG or PCI) and all-cause mortality. Results Age (42±12 yrs) and proportion of male (40%) was lower in healthy individuals compared to individuals with CVRF (54±11 yrs, 45% male) or with CVD (57±13 yrs, 62% male). During a median follow-up of 7 years (IQR 6–9), 2,485 events occurred in healthy individuals (2% of n=112,018), 2,214 in individuals with CVRF (8% of n=27,982) and 1,100 (36% of n=3,059) in those with CVD. Higher PA volumes were associated with a lower risk of adverse outcomes in healthy individuals and in individuals with CVRF (Table 1). In contrast, only the highest PA quartile was associated with a risk reduction for adverse outcomes in individuals with CVD (Table 1). Also, effect-modification was present in the dose-response relationship between PA volumes and health outcomes for CVD (P-interaction<0.05), but not for healthy or CVRF. Conclusions Cardiovascular health status impacts the dose-response relationship between PA volumes and adverse outcomes. These findings indicate that PA recommendations should be adjusted to an individual's health status for achieving maximal health benefits from a physically active lifestyle. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): The work of T.M.H.E is supported by the Netherlands Heart Foundation. The Lifelines Biobank initiative received a subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen [UMCG], University Groningen and the Northern Provinces of the Netherlands.

The unborn smoker: association between smoking during pregnancy and adverse perinatal outcomes

2015 ◽  
Vol 43 (5) ◽  
Author(s):  
Elad Mei-Dan ◽  
Asnat Walfisch ◽  
Boaz Weisz ◽  
Mordechai Hallak ◽  
Richard Brown ◽  
...  
Keyword(s):  
Preterm Delivery ◽  
Dose Response ◽  
Reference Group ◽  
Adverse Outcomes ◽  
Delivery Rate ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Fetal Demise ◽  
Smoking During Pregnancy ◽  
Intrauterine Fetal Demise

AbstractTo evaluate a possible dose-response relationship between active maternal smoking during pregnancy and adverse perinatal outcome.Retrospective cohort study.Population-based in Montreal, Quebec, Canada.Women who gave birth to a liveborn or stillborn infant during the period of January 2001 to December 2007.Active smokers of different daily cigarette consumption (n=1646) were identified through maternal self-reporting. The reference group comprised 19,292 non-smoking women who delivered during the same period.Birth weight, preterm delivery rate, fetal and neonatal mortality and morbidity, and congenital malformations.Preterm delivery rate was significantly higher in the smoking group compared with controls (22.2% vs. 12.4%, P<0.05), as was intrauterine fetal demise (1.4% vs. 0.3%, P<0.05). Newborns of active smokers were more likely to weigh less (3150±759 g vs. 3377±604 g, P<0.05), suffer from respiratory distress syndrome (2.5% vs. 1.3%, P<0.05), suffer from a cardiac malformation (1.5% vs. 0.8%, P<0.05), and die (neonatal death 1.2% vs. 0.6%, P<0.05). A dose-response relationship was demonstrated between levels of daily cigarette smoking and several adverse outcomes. Using multiple regression models, smoking was found to be an independent predictor of preterm delivery (odds ratios (OR) 1.9, 95% confidence intervals (95%CI) 1.6–2), and intrauterine fetal demise (OR 2.4, 95%CI 1.4–4.2).Any amount of daily smoking appears to harm the fetus and newborn. As pregnancy may be a “window of opportunity” for behavioural changes, efforts to promote smoking cessation should be encouraged.

scholarly journals Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach (Preprint)

2019 ◽  
Author(s):  
Li-Ning Peng ◽  
Fei-Yuan Hsiao ◽  
Wei-Ju Lee ◽  
Shih-Tsung Huang ◽  
Liang-Kung Chen
Keyword(s):  
Machine Learning ◽  
Dose Response ◽  
Frailty Index ◽  
Risk Groups ◽  
Adverse Outcomes ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Machine Learning Approach ◽  
Unplanned Hospitalizations

BACKGROUND Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. OBJECTIVE This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. METHODS In this study, we used Taiwan’s National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. RESULTS The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (<i>P</i>&lt;.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. CONCLUSIONS The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.

scholarly journals Dose-response relationship between very vigorous physical activity and cardiovascular health assessed by heart rate variability in adults: Cross-sectional results from the EPIMOV study

PLoS ONE ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. e0210216 ◽  
Author(s):  
Thiago Luís Wanderley de Sousa ◽  
Thatiane Lopes Valentim di Paschoale Ostoli ◽  
Evandro Fornias Sperandio ◽  
Rodolfo Leite Arantes ◽  
Antônio Ricardo de Toledo Gagliardi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Dose Response ◽  
Cardiovascular Health ◽  
Vigorous Physical Activity ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Cross Sectional

scholarly journals Comparisons Between Hypothesis- and Data-Driven Approaches for Multimorbidity Frailty Index: A Machine Learning Approach

10.2196/16213 ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. e16213
Author(s):  
Li-Ning Peng ◽  
Fei-Yuan Hsiao ◽  
Wei-Ju Lee ◽  
Shih-Tsung Huang ◽  
Liang-Kung Chen
Keyword(s):  
Machine Learning ◽  
Dose Response ◽  
Frailty Index ◽  
Risk Groups ◽  
Adverse Outcomes ◽  
Response Relationship ◽  
Dose Response Relationship ◽  
Machine Learning Approach ◽  
Unplanned Hospitalizations

Background Using big data and the theory of cumulative deficits to develop the multimorbidity frailty index (mFI) has become a widely accepted approach in public health and health care services. However, constructing the mFI using the most critical determinants and stratifying different risk groups with dose-response relationships remain major challenges in clinical practice. Objective This study aimed to develop the mFI by using machine learning methods that select variables based on the optimal fitness of the model. In addition, we aimed to further establish 4 entities of risk using a machine learning approach that would achieve the best distinction between groups and demonstrate the dose-response relationship. Methods In this study, we used Taiwan’s National Health Insurance Research Database to develop a machine learning multimorbidity frailty index (ML-mFI) using the theory of cumulative diseases/deficits of an individual older person. Compared to the conventional mFI, in which the selection of diseases/deficits is based on expert opinion, we adopted the random forest method to select the most influential diseases/deficits that predict adverse outcomes for older people. To ensure that the survival curves showed a dose-response relationship with overlap during the follow-up, we developed the distance index and coverage index, which can be used at any time point to classify the ML-mFI of all subjects into the categories of fit, mild frailty, moderate frailty, and severe frailty. Survival analysis was conducted to evaluate the ability of the ML-mFI to predict adverse outcomes, such as unplanned hospitalizations, intensive care unit (ICU) admissions, and mortality. Results The final ML-mFI model contained 38 diseases/deficits. Compared with conventional mFI, both indices had similar distribution patterns by age and sex; however, among people aged 65 to 69 years, the mean mFI and ML-mFI were 0.037 (SD 0.048) and 0.0070 (SD 0.0254), respectively. The difference may result from discrepancies in the diseases/deficits selected in the mFI and the ML-mFI. A total of 86,133 subjects aged 65 to 100 years were included in this study and were categorized into 4 groups according to the ML-mFI. Both the Kaplan-Meier survival curves and Cox models showed that the ML-mFI significantly predicted all outcomes of interest, including all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions at 1, 5, and 8 years of follow-up (P<.01). In particular, a dose-response relationship was revealed between the 4 ML-mFI groups and adverse outcomes. Conclusions The ML-mFI consists of 38 diseases/deficits that can successfully stratify risk groups associated with all-cause mortality, unplanned hospitalizations, and all-cause ICU admissions in older people, which indicates that precise, patient-centered medical care can be a reality in an aging society.

A SUBCUTANEOUS INJECTION ASSAY FOR ANTI-ANDROGENS IN THE CHICK

1962 ◽  
Vol 41 (2) ◽  
pp. 268-273 ◽  
Author(s):  
Ralph I. Dorfman
Keyword(s):  
Dose Response ◽  
Subcutaneous Injection ◽  
Response Relationship ◽  
Dose Response Relationship

ABSTRACT The stimulating action of testosterone on the chick's comb can be inhibited by the subcutaneous injection of 0.1 mg of norethisterone or Ro 2-7239 (2-acetyl-7-oxo-1,2,3,4,4a,4b,5,6,7,9,10,10a-dodecahydrophenanthrene), 0.5 mg of cortisol or progesterone, and by 4.5 mg of Mer-25 (1-(p-2-diethylaminoethoxyphenyl)-1-phenyl-2-p-methoxyphenyl ethanol). No dose response relationship could be established. Norethisterone was the most active anti-androgen by this test.

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