Showing posts with label meta-analysis. Show all posts
Showing posts with label meta-analysis. Show all posts

Sunday, July 24, 2016

Global Sodium Consumption and Death from Cardiovascular Causes

INTRODUCTION
- A high dietary intake of Sodium is associated with elevated blood pressure, a major risk factor for cardiovascular disease.
- The United Nations (UN), World Health Organization (WHO), Centers for Disease Control and Prevention (CDC) and other organizations have emphasized the relationship between dietary sodium and cardiovascular outcomes.

- As part of the Global Burden of Diseases Nutrition and Chronic Diseases Expert Group
(NUTRICODE), we
i) Systematically identified and analyzed data on sodium consumption worldwide and calculated the dose-response effects of sodium on blood pressure in a new meta-analysis of trials.
ii) Compiled data to calculate the effects of blood pressure on cause-specific cardiovascular mortality and to characterize current blood-pressure levels and numbers of cause-specific deaths according to country, age, and sex
iii) Used data relating levels of sodium intake to blood pressure and cardiovascular events, as well as data on the lowest current levels of sodium intake according to country, to define a reference range for sodium consumption
iv) Used data (Table S1 in the Supplementary Appendix) to estimate the impact of current levels of sodium intake on cardiovascular mortality throughout the world

METHODS

Assessment of Global Sodium Consumption
- Search Period: March 2008 to December 2011
- Systematic searches were conducted for previously conducted national or subnational surveys on individual-level sodium consumption based on urinary excretion, estimated dietary intake, or both
- Data Included: 142 surveys with data from 24-hour urine collections and 91 with estimates of dietary intake, including 28 with both types of data (Table S2 in the Supplementary Appendix).
- These surveys included data from 66 countries, accounting for 74.1% of adults in the world.
- Using a hierarchical Bayesian model, we estimated the mean level of sodium consumption and statistical uncertainty according to age, sex, and calendar year in 187 nations.
- An article with detailed results of these analyses has been published previously (Powles et al. BMJ Open 2013; 3(12): e003733)

- Our model estimated sodium consumption with the use of 24-hour urine collections as the reference standard.
- To make our data comparable to data from prior regional surveys and blood pressure trials in which urinary sodium levels were measured, we did not adjust our analyses for sodium loss due to factors other than urinary excretion (e.g., sweat).

Effects of Reduced Sodium Intake on Blood Pressure
- Two recent Cochrane meta-analyses evaluated randomized trials of the effect of reduce sodium intake on blood pressure. (He et al. Cochrane Database Syst Rev
2013; 4: CD004937; Graudal et al. Cochrane Database Syst Rev 2011;11: CD004022)
- These meta-analyses did not determine whether blood-pressure lowering was linear across a range of sodium intakes and did not simultaneously quantify heterogeneity according to age, race, and the presence or absence of hypertension
- We performed a new meta-analysis evaluating all randomized interventions identified in these articles (Section S1 in Supplementary Appendix).
- Using data from these trials, we evaluated whether the effects of reduced sodium intake on blood pressure were linear.
- We evaluated the potential heterogeneity in this effect by taking into account population characteristics, including age, the presence or absence of hypertension, and race, as well as the duration of the intervention.
- We also assessed whether, apart from the presence or absence of hypertension, the effects of reduced sodium intake on blood-pressure lowering were blunted by the use of antihypertensive medication.

Effects of Blood-Pressure Levels on Cardiovascular Mortality
- To calculate the effects of systolic blood pressure on deaths from cardiovascular causes, we combined results from two large international projects (totaling 99 cohorts, 1.38 million participants, and 65,000 cardiovascular events) that pooled individual-level data, consistently adjusted for confounding.
- We accounted for regression dilution bias based on serial blood-pressure measures over time.
- We interpolated and extrapolated age-specific proportional effects (relative risks) of systolic blood pressure on cardiovascular mortality in 10-year age groups across the pooling projects (see Section S2 and Fig. S3 in the Supplementary Appendix).
- We used the same estimates of relative risk according to sex and race, on the basis of evidence of generally similar proportional effects of blood pressure on cardiovascular events according to sex and race in trials of antihypertensive drugs and observational studies of blood pressure and cardiovascular events.

Reference Levels of Sodium Consumption
- To define reference levels of sodium consumption, we conducted a search of published survey data, cohort studies, controlled trials, and dietary recommendations.
- We determined levels of sodium consumption that were associated with the lowest blood-pressure levels in ecologic studies and in randomized trials and with the lowest risk of disease in meta-analyses of prospective cohort studies
- We also considered at least theoretical feasibility based on the lowest national mean levels of consumption globally.
- Finally, we considered the consistency of our identified reference intake levels with major dietary guidelines.
- Details are provided in Section S4 in the Supplementary Appendix

Current Blood-Pressure Levels and Cause-Specific Mortality
- Data on current blood-pressure levels and cardiovascular mortality, each according to country, age, and sex, were compiled as part of the Global Burden of Disease Study 2010
- Data on blood pressure (from 786 country-years and 5.4 million participants) were obtained from published and unpublished health examination surveys and epidemiologic studies from around the world.
- Data on causes of death were obtained for 187 countries from 1980 through 2010; these data were obtained from vital-registration systems, verbal autopsies, mortality surveillance, census data, surveys, hospitals, police records, and mortuaries.
- Details of data collection and the statistical modeling used to estimate mean systolic blood pressure and causespecific mortality are provided in Table S1 and Sections S5 and S6 in the Supplementary Appendix

Cardiovascular Mortality Associated with Sodium Consumption above the Reference Level
- We estimated disease burdens using comparative risk assessment, capturing geographic and demographic variations in sodium intake, blood pressure, cardiovascular mortality, and corresponding uncertainties (details are provided in Table S1 and Section S7 in the Supplementary Appendix).
- We incorporated age-specific and sex-specific sodium intake, blood-pressure level, relative risk, and mortality data for each country to model the fraction and numbers of deaths estimated to be attributable to sodium intake above the reference level.

- The population-attributable fraction was estimated in a two-step process.
- First, we used the effects of sodium consumption on blood pressure according to age, the presence or absence of hypertension, and race to calculate the change in mean systolic blood pressure that would be expected from reducing sodium consumption to reference levels as defined above.
- Second, we used the age-specific effects of blood pressure on cardiovascular mortality to calculate the resulting change in risk.
- Estimated numbers of deaths attributable to sodium intake above the reference level were calculated by multiplying the population-attributable fraction by the absolute number of deaths in each country, age, and sex stratum.

RESULTS

Global Sodium Consumption
- We estimated that in 2010, the mean level of consumption of sodium worldwide was 3.95 g per day, and regional means ranged from 2.18 to 5.51 g per day (Fig. S1 in the Supplementary Appendix).
- Overall, 181 of 187 countries — 99.2% of the adult population in the world — had estimated mean levels of sodium intake exceeding the World Health Organization recommendation of 2.0 g per day, and 119 countries — 88.3% of the adult population in the world — exceeded this recommended level by more than 1.0 g per day.

Effects of Reduced Sodium Intake on Blood Pressure
- In our primary analysis of reduced sodium intake and blood pressure, we found strong evidence of a linear dose–response relationship (P < 0.001 for linearity and P = 0.58 for nonlinearity) (Fig. 1A).
- When the data were evaluated with the use of inverse-variance weighted meta-regression, each reduction of 2.30 g of sodium per day was associated with a reduction of 3.82 mm Hg (95% confidence interval [CI], 3.08 to 4.55) in blood pressure (Fig. 1B).


- The effects of dietary sodium on blood pressure were modified according to population characteristics, with larger reductions in blood pressure among (Fig. S2 in the Supplementary Appendix):
i) older persons >  younger persons
ii) blacks > whites,
iii) hypertensive > normotensive persons.
- For a white, normotensive population at 50 years of age, each reduction of 2.30 g per day in sodium intake lowered systolic blood pressure by 3.74 mm Hg (95% CI, 2.29 to 5.18).
- We did not find evidence of substantial blunting of the blood-pressure–lowering effects of sodium restriction by antihypertensive drugs, although the data available to address this question were limited. Further details are provided in Section S1 in the Supplementary Appendix

Effects of Blood Pressure on Cardiovascular Mortality
- The pooled analyses of blood pressure and cardiovascular mortality showed a log-linear (proportional) dose–response relationship, with no evidence of a threshold as low as a systolic blood pressure of at least 115 mm Hg (see Section S2 and Fig. S3 in the Supplementary Appendix).
- The relative magnitude of the effect on blood pressure decreased with age, in a manner similar to that seen with other cardiovascular risk factors.

Reference Levels of Sodium Consumption
- Potential reference levels of sodium consumption according to various definitions are shown in Table S3 in the Supplementary Appendix
- The lowest mean intake associated with both lower systolic blood pressure and a lower positive relationship between higher age and blood pressure in ecologic studies was 614 mg of sodium per day.
- In large, well-controlled, randomized feeding trials, the lowest tested sodium intake for which reductions in blood-pressure levels were clearly documented was 1500 mg per day.
- In prospective observational studies, the lowest mean sodium intake associated with a lower risk of cardiovascular events ranged from 1787 to 2391 mg per day.
- We also considered observed mean levels of sodium intake that have been associated with the lowest risk of stomach cancer (1245 mg per day).
- Levels of sodium intake associated with the lowest risk ranged from 614 to 2391 mg per day, depending on the type of evidence and the outcome.
- According to national data on sodium consumption, the estimated lowest observed mean national intake level was approximately 1500 mg per day.
- The maximum level of sodium intake recommended in major dietary guidelines ranged from 1200 to 2400 mg per day


Estimated Cardiovascular Mortality Attributed to Sodium Consumption
- On the basis of the correlations between sodium intake and blood pressure and between blood pressure and cardiovascular mortality that are described above, and using a reference level of sodium intake of 2.0±0.2 g per day, we found that 1.65 million deaths from cardiovascular causes (95% uncertainty interval, 1.10 million to 2.22 million) worldwide in 2010 were attributable to sodium consumption above the reference level (Table 1, and Table S4 in the Supplementary Appendix).

Of these deaths,
- 687,000 (41.7%) were due to coronary heart disease,
- 685,000 (41.6%) were due to stroke, and
- 276,000 (16.7%) were due to other cardiovascular disease.
- Globally, 40.4% of these deaths occurred prematurely (i.e., in persons younger than 70 years of age) (see Section S8 and Fig. S4 in the Supplementary Appendix).
- Four of every 5 sodium-associated deaths from cardiovascular causes (84.3%) occurred in low-income and middle-income countries.
- In sum, approximately 1 of every 10 deaths from cardiovascular causes worldwide (9.5%) (95% uncertainty interval, 6.4 to 12.8) and nearly 1 of every 5 (17.8%) premature deaths from cardiovascular causes were attributed to sodium consumption above the reference level.

- Across nine regions of the world, the absolute rate of sodium-associated deaths from cardiovascular causes was highest in Central Asia and Eastern and Central Europe (Fig. 2A, and Fig. S5 and Table S4 in the Supplementary Appendix).
- Proportional cardiovascular mortality was high in all regions: among younger adults, it exceeded 10% in nearly all regions and it exceeded 20% in Central Asia and Eastern and Central Europe, East Asia, and Southeast Asia (Fig. 2B).
- Among older adults, who have a higher absolute risk and more competing risk factors, proportional sodium-associated cardiovascular mortality approached or exceeded 10% in Central Asia and Eastern and Central Europe, East Asia, and Southeast Asia.
- Most sodium-associated cardiovascular deaths were due to coronary heart disease, except in East Asia, Southeast Asia, and sub-Saharan Africa, where most deaths from cardiovascular causes were due to stroke, especially hemorrhagic and other nonischemic strokes (Table S4 and Fig. S5 in the Supplementary Appendix). 


- Across individual nations, substantial variation was evident.
- Sodium-associated cardiovascular mortality was highest in the country of Georgia (1967 deaths per 1 million adults per year; 95% uncertainty interval, 1321 to 2647) and lowest in Kenya (4 deaths per 1 million adults per year; 95% uncertainty interval, 3 to 6) (Fig. 3).
- Proportional cardiovascular mortality ranged from 27.4% in Mauritius (95% uncertainty interval, 18.8 to 35.9) to 0.3% in Kenya (95% uncertainty interval, 0.2 to 0.4) (Fig. 4).


-Among the 30 most populous nations (Fig. S6 in the Supplementary Appendix), the highest sodium-associated cardiovascular mortality was in Ukraine (1540 deaths per 1 million adults per year; 95% uncertainty interval, 1017 to 2099), and the highest proportional mortality was in China (15.3% of all cardiovascular deaths; 95% uncertainty interval, 10.5 to 20.2).
- Detailed information about individual nations is provided in Section S9 and Table S5 in the Supplementary Appendix.

- In sensitivity analyses, lowering the definition of the reference intake level from 2.0 to 1.0 g of sodium per day increased the number of deaths from cardiovascular causes in the world that were attributed to sodium consumption by approximately 40%, to 2.30 million (95% uncertainty interval, 1.55 million to 3.07 million) (Tables S6 and S7 and Fig. S7 and S8 in the Supplementary Appendix).
- When we estimated effects attributable only to sodium intake above 4.0±0.4 g per day, 512,901 worldwide deaths from cardiovascular causes (95% uncertainty interval, 333,710 to 704,773) were attributed to such consumption (Tables S8 and S9 in the Supplementary Appendix).
- This was the estimated number of deaths that were potentially preventable if only the nations with the highest level of sodium consumption lowered their intake to just the current mean intake in the world.
- If we altered our model so that the estimated benefits of blood-pressure lowering did not continue below 125 mm Hg, 1.55 million deaths from cardiovascular causes in the world (95% uncertainty interval, 1.10 million to 2.10 million) were attributed to sodium consumption above a level of 2.0 g per day.

DISCUSSION

Main Findings
- Globally, 1.65 million deaths from cardiovascular causes in 2010 — about 1 of 10 deaths from cardiovascular causes — were attributed to sodium consumption of more than 2.0 g per day.
- Notably, 4 of 5 of these deaths occurred in low and middle-income countries, and 2 of 5 of these deaths occurred prematurely (before the age of 70 years).

Regional Findings
- Our findings also show and quantify the heterogeneity in disease burden attributed to sodium according to region, age, and type of cardiovascular disease.
- Yet, we also found that no region and few countries were spared.
- Whereas estimated sodium-associated cardiovascular mortality was highest in Central Asia, it was high (more than 750 deaths per 1 million adults who were 70 years of age or older) in all regions.
- The estimated number of proportional sodium-associated deaths was also high, approaching or exceeding 15% of premature deaths from cardiovascular causes in most regions

Dose-response relationship between sodium intake and blood pressure
- Our meta-analysis of 107 randomized interventions in 103 trials showed a linear dose–response relationship between reduced sodium intake and blood pressure, jointly modified according to age, race, and the presence or absence of hypertension.
- These findings are consistent with the findings of a meta-analysis, published after submission of this article, that included fewer trials (34 trials).
- Larger effects in older adults and hypertensive persons would be consistent with decreasing vascular compliance and renal filtration; in blacks, larger effects would be consistent with differences in renal handling of sodium.
- We used randomized trials of reduced sodium intake and blood pressure to estimate the more conceptually appropriate effect of lifetime differences in intake, because direct evidence on lifetime effects, which may be larger, is available only from ecologic comparisons and experiments involving nonhuman primates

Possible controversy
- Some researchers have argued that it may not be possible to directly extrapolate the effects of sodium on blood pressure to cardiovascular risk.
- However, the effect on cardiovascular disease is supported by extensive experimental and ecologic evidence, data on cardiovascular events from some trials of reduced sodium intake, and evidence of the cardiovascular benefits of blood-pressure lowering across multiple interventions (see Section S3 in the Supplementary Appendix).
- A meta-analysis of prospective cohort studies showed that higher sodium consumption was associated with a higher rate of death from coronary heart disease (relative risk, 1.32; 95% CI, 1.13 to 1.53) and death from stroke (relative risk, 1.63; 95% CI, 1.27 to 2.10), the two main end points in our analysis.
- Although concerns have been raised that reduced sodium intake may cause physiological harm, a meta-analysis of 37 trials showed no significant adverse effects on blood lipid levels, catecholamine levels, or renal function

Results from observational studies
- There is mixed evidence from observational data on the relationship between very low sodium intake and cardiovascular events.
- A recent Institute of Medicine report concluded that, if restricted to studies of clinical cardiovascular events, there is insufficient evidence that lowering sodium intake further beyond 2.30 g per day either increases or decreases the occurrence of cardiovascular disease.
- Yet the report further concluded that the entirety of the evidence, “when considered collectively, indicates a positive relationship between higher levels of sodium intake and [the] risk of cardiovascular disease.”
- Although precise targets for sodium reduction remain controversial, various organizations tasked with reviewing all the evidence have arrived at target levels ranging from 1200 to 2400 mg per day (Table S3 in the Supplementary Appendix).

Limitations
- Causality cannot be proved, although every effort was made to maximize validity, minimize error and bias, and incorporate heterogeneity and uncertainty,
- Dietary sodium was estimated based on 24-hour urine collections, which reflect approximately 90% of intake and also can be limited by incomplete collection.
- Data on sodium intake were not available across all countries or years - increased statistical uncertainty and the risk that some data could reflect sampling bias.
- Dietary sodium is also associated with nonfatal cardiovascular disease, kidney disease, and gastric cancer, the second-leading fatal cancer worldwide – may underestimate the full global health effects of dietary sodium.
- No data on potassium consumption – also influences blood pressure and the risk of stroke.
- Specific approaches or timelines for reduced sodium intake was not incorporated.

CONCLUSION
- On the basis of currently available data on sodium consumption, dose–response effects on blood pressure and cardiovascular mortality, and cause-specific deaths, we estimate that in 2010, a total of 1.65 million deaths from cardiovascular causes were attributable to consumption of more than 2.0 g of sodium per day.



Correspondence (Comment by other experts and the authors' reply)

Thursday, July 7, 2016

Short sleep duration and obesity among children: A systematic review and meta-analysis of prospective studies

Objective
- To systematically review published population-based studies,
- To carry out a meta-analysis to assess whether the epidemiology evidence from longitudinal studies supports the presence of a relationship between short sleep duration and obesity in children and adolescents.

Method
- Database: Medline, Cochrane Library, EMBASE and Science Citation Index
- Date: From Jan 1980 through Jan 2015
- No language restrictions
- Included studies: cohort studies, with a measure of OR/RR/HR, reported on overweight/obesity, exposure of interest was sleep duration, f/up for at least 1 year, children or adolescent
- Quality Assessment: Newcastle-OttawaScale (NOS), a 9-point scale that cohorts (0-4 points), the comparability of cohorts (0-2 points), and the identification of the exposure and the outcomes of study participants (0-3 points). Scores: 0-3, 4-6, and 7-9 for low, moderate, and high quality studies, respectively

Definition:
i) Obesity/Overweight was defined either as BMI ≥95th percentile or BMI ≥85th percentile or BMI ≥25 kg/m2 or BMI ≥ 30 kg/m2 or the International Obesity Task Force (IOTF) BMI cut-offs
ii) Short sleep duration (depend on study): <12h or 11.5h or <10.5h or <10h or <8h or <7.5h, or <6h per night; or <50th or <25th percentile; or unclear
The authors categorized short sleep duration and reference sleep duration using the recommendations of Sleep Health Foundation (SHF)

Results
Characteristics of Included Studies
- 13 studies (50 cohorts; n=35,540), published between 2005-2013
- Study locations: 6 in US, 2 in Europe, 2 in Australia and 3 in Canada
- All studies assessed sleep duration by questionnaire and obesity outcome by BMI
- Median quality scores: 5.5 (range 4-7)
- Major confounding factors adjusted were: age, gender, BMI, physical activity and parental income

Main analysis
- Short sleep duration was significantly associated with the risk of future obesity (OR, 1.71; CI, 1.36-2.14; I2= 91.3%)
- The association (OR, 1.72; CI, 1.35-2.18; I2= 91.8%) did not change materially after excluding a large sample size study
- All studies didn’t report separate results for male and female. Thus, pooled measures were not calculated separately by gender for these studies


Subgroup analysis
i) Follow-up years: OR 1.88 (95% CI, 1.57-2.24) for < 5 years and 1.43 (95% CI, 1.09-1.88) for ≥ 5 years)
ii) Study location: Significant association was found among children in USA, Canada, Europe and Australia
iii) SHF recommendations: The effect appeared similar between short sleep groups of ≥SHF recommendation (OR, 1.50; 95%CI, 1.21-1.85) and other category (OR, 1.81; 95% CI, 1.35-2.42) was significantly associated with obesity. The significant association was also found in the reference sleep duration ≥SHF recommendation reported the strongest association (OR, 1.89; 95%CI, 1.59-2.23), but not in other category (OR, 1.28; 95%CI, 0.95-1.72).
-The heterogeneity of effect may due to differences in geographical location, cut-off for short sleep duration and definition of obesity/overweight

Sensitivity analysis
- Excluded each single study in turn and pooled the results of the remaining included studies, the summary OR of obesity ranged from 1.63 (1.32-2.01) to 1.78(1.52-2.08) for short sleep duration
- No individual study had excessive influence on the pooled effect between risk of obesity and sleep duration

Publication bias
- No significant publication bias for the analysis between obesity risk and sleep duration (Egger test intercept = 1.76; P = 0.07; Duvall and Tweedie adjusted OR, 1.53; 95% CI, 1.25-2.00; number of imputed studies = 0)

Discussion
- Children and adolescents who sleep fora shorter duration have approximately twice the odds of overweight/obesity compared with those who sleep for longer duration.
-Several potential causal pathways linking short sleep duration with obesity have been suggested based on experimental studies of sleep deprivation:
i) Sleep deprivation might predispose to weight gain is by increasing caloric intake- Animal studies found that sleep deprivation produces hyperplasia;
ii) Associations between inadequate sleep and alterations in leptin and ghrelin, which lead to increased appetite and subsequently increased food intake;
iii) Short sleep duration could lead to obesity by increasing the time available to eat, and
iv) Decrease energy expenditure by increasing fatigue as well as changes in thermoregulation

Limitation
- High level of heterogeneity
- None of the studies included patients from Africa, Latin America or China, which may limit the generalisability of our results to African or other populations.
- Information on sleep duration and obesity/overweight was self-reported by questionnaires in all selected studies

Conclusion
- Short sleep duration was significantly associated with future obesity among children/adolescents.
- Weight gain appears to be a late consequence of short sleep duration.
- Sleep duration has been recognised as an easily amendable risk factor for obesity, which increasing sleep among children may play an important role in prevention and treatment of obesity


Sunday, June 19, 2016

BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants

Introduction:
- The association between overweight and obesity and increased risk of a wide range of chronic diseases, including cardiovascular diseases, type 2 diabetes, several types of cancer, gallbladder disease, gout, osteoarthritis, and several other conditions, as well as all cause mortality.
- Though many studies have shown an increased risk of all cause mortality with greater adiposity as measured by body mass index (BMI), questions remain about the shape of the dose-response relation
- It is well known that smoking strongly increases risk of mortality and many specific causes of death, and there is therefore a great potential for residual confounding by smoking as it is typically also associated with lower weight.
- Weight loss can precede a diagnosis of disease by many years and because of such preclinical weight loss the associations between low BMI and increased mortality might at least partly be caused by confounding by preclinical disease.

Objective:
- To demonstrate the association of BMI and all cause mortality
- To clarify the shape and the nadir of the dose-response curve
- To study the influence of confounding from smoking, weight loss associated with disease and preclinical diseases

Methods:
- Search Period: until 23 September 2015
- Database: PubMed and Embase
- Language: English
- Study quality assessment: Newcastle-Ottawa scale
- Inclusion criteria: Cohort studies reported adjusted risk estimates for at least 3 BMI categories in relation to all cause mortality; populations living in the community
- Exclusion criteria: studies included only patients, nursing home residents, and disabled people; only reported continuous linear risk estimate (because there is evidence that the association between BMI and mortality is non-linear)
- Other consideration: we did not include data from studies that combined never smokers and former smokers who had quit for a long duration
Statistics:
- Primary analysis: Never smokers
- Relative risk calculated with random effects models
- Non-linear associations were explored with fractional polynomial models.
- The analyses were re-scaled so the reference category was a BMI of 23, which seemed to be the nadir of the curve among never smokers, so there was no loss of statistical power from these re-calculations

Results:
- Total number of included studies: 230 cohort studies (207 publications)
- Total number of subjects: 30 233 329 participants (>3 744 722 deaths)\
- Study Location: Europe (n=96), North America, (n=71) Latin or South America (n=3)
Asia (n=49), Australia and New Zealand (n=10), Pacific region (n=1)
Among Never Smokers,
- Number of included studies: 53 cohort studies (with 44 risk estimates)
- Number of subjects: > 9 976 077 participants (>738 144 deaths)

Main findings:
For every 5 unit increment in BMI, the relative risk (RR) for all cause mortality among:
- Never smokers (primary analysis): RR 1.18 (95% CI 1.15-1.21; I2=95%; n=44)
- Among healthy* Never smokers: RR 1.21 (95% CI 1.18-1.25; I2=93%; n=25)
- Among healthy Never smokers with exclusion of early follow-up# : RR 1.27 (95% CI 1.21-1.33; I2=89%; n=11)
- All participants: RR 1.05 (95% CI 1.04-1.07; I2=97%; n=198)

There was a J shaped dose-response relation in never smokers and lowest risk was observed at (reference at BMI 23):
- BMI 23-24 in never smokers
- BMI 22-23 in healthy never smokers
- BMI 20-22 in studies of never smokers with ≥20 years of follow-up

There was a U shaped association between BMI and mortality in analyses with a greater potential for bias, including
- All participants
- Current, former or ever smokers
- In studies with short duration of follow-up (<5 years or <10 years)
- Moderate study quality scores

Publication bias: Absent (never smoker analysis), Present (All participants analysis)

Subgroup analysis:
- Stratified by sex, geographical region, study quality scores, number of deaths: Never smokers (absence of heterogeneity); all participants (presence of heterogeneity)
- The positive association between BMI and all cause mortality among never smokers persisted in subgroup analyses defined by sex, assessment of anthropometric measures, geographical location, number of deaths, and adjustment for confounding factors including age, education, alcohol, physical activity, height, dietary pattern, and intake of fat, fruit, and vegetables. There was little evidence of heterogeneity between any of these subgroups with meta-regression analyses
- In the analysis of all participants, heterogeneity is presence among studies stratified by adjustment for number of cigarettes smoked a day (P<0.001), with a stronger association among studies with such adjustment compared with studies without such adjustment
- The association between BMI and mortality was stronger among studies that had adjusted for the most important confounding factors (age, smoking, alcohol, physical activity) but that did not adjust for intermediate factors or prevalent disease. These associations were further strengthened among studies with longer duration of follow-up
- Among Never smokers: the association between BMI and all cause mortality was considerably stronger among people aged <65 than among people aged ≥65 (sig heterogeneity)

Discussion:
- Among never smokers: relative risks of 1.11, 1.24, 1.42, 1.98 and 3.54 for BMI values of 27.5, 30, 32.5, 37.5, and 45 compared with a BMI of 23 (Table)
- The analysis of all participants needs to be interpreted carefully as there is a greater possibility of confounding by smoking and confounding from prediagnostic weight loss associated with disease.
- The increased risk observed with a BMI of 20 in the analysis of all participants and never smokers and the lower risk in overweight people in the analysis of all participants is likely to be caused by confounding by smoking and prediagnostic weight loss.
- The lowest risk was observed in the overweight range at a BMI of 27.5 in the studies with medium quality scores, while the lowest risk was observed at a BMI of 24-25 in the studies with the highest quality scores.

Limitations:
- Meta-analysis of observational studies: confounding by unmeasured or imperfectly measured risk factors could have influenced the results
- Not able to investigate the potential interactions between BMI and physical activity or dietary factors in relation to all cause mortality as few studies reported such results
- Although BMI is an imperfect measure of body fatness but in most people it is highly correlated with measures of body fat; incorporating waist measures might have additional clinical relevance for risk assessment
- Measurement errors in the assessment of height and weight could have influenced the results.
- Only English language publication

Conclusion:
- Overweight and obesity is associated with increased risk of all cause mortality and the nadir (lowest point=lowest mortality) of the curve was observed at BMI 23-24 among never smokers, 22-23 among healthy never smokers, and 20-22 with longer durations of follow-up.
- The increased risk of mortality observed in underweight people could at least partly be caused by residual confounding from pre-diagnostic disease.
- Lack of exclusion of ever smokers, people with prevalent disease, and early follow-up and inclusion of studies with lower study quality could bias the associations between BMI and mortality towards a more U shaped association

Note:
- For relative risk or odds ratio, the result is significant if the 95% confidence interval does not include 1, because 1 means null effect.
(Some study will only report 95% CI without p-value, it is beneficial to learn how to determine significant without relying on p-value. Try to look at the results for this study and determine whether the result is significant.)
- Heterogeneity (I2≥50% or p-value for I2 <0.05) may weaken the result of a meta-analysis. They are usually addressed in subgroup/sensitivity analysis.

* Healthy: Excluded people with prevalent cancer, cardiovascular disease, and in some cases diabetes, and/or people with recent weight loss)
# Excluded early follow-up: (from first year up to six years of follow-up)




Aune et al. BMJ 2016;353:i2156. http://dx.doi.org/10.1136/bmj.i2156

Tuesday, June 14, 2016

Fruit and vegetable consumption and the risk of depression: A meta-analysis

Introduction
- Some lifestyle behaviors have been reported to increase the risk of depression, such as sedentary behavior, long-term stress, and short or long hours of sleep.
- In recent years, the association between diet and depression has also drawn much attention. 
- Among the dietary factors, consumption of fruits and vegetables, which are rich in antioxidants and anti-inflammatory components, was hypothesized to play an important role in depression development

Method
-Search Period: until June 2015
-Inclusion criteria: observational study, Exposure: fruits / vegetables, Outcome: depreesion (determined by self-report based on doctor’s diagnosis, regular use of antidepressant drugs, or depression rating scales)
-Exclusion criteria: postpartum depression

Results
- Number of studies included: 10 (4 cohort, 7 cross-sectional)

1) Fruit intake & risk of depression
- Included studies: 4 cohort & 6 cross-sectional (n=227, 852 participants)
- Study location: Asia (n=3), Europe (n=4), Oceania (n=1), North America (n=1), South America (n=1)
Highest vs lowest category of fruit intake:
- Overall analysis: RR 0.86 (95% CI, 0.81-0.91; p<0.01), I2=48.2%; p=0.043
- Subgroup analysis (cross sectional studies): RR 0.84 (95% CI, 0.75-0.95; p=0.006), I2=55.7%; p=0.046
- Subgroup analysis (cohort studies): RR 0.83 (95% CI, 0.77-0.91; p=0.001), I2=41.2%; p=0.164
- Meta-regression: did not find any a priori determined covariates as the important contributors to the between-study heterogeneity 
- Leave-one-out sensitivity analysis: after excluding one study, heterogeneity of fruits studies decreased to 27.5% and the results still remained significant – therefore, the result is stable.

2) Vegetable intake & risk of depression
- Included studies: 4 cohort & 4 cross-sectional (n=218, 699 participants)
- Study location: Asia (n=3), Europe (n=2), Oceania (n=1), North America (n=1), South America (n=1)
Highest vs lowest category of vegetables intake:
- Overall analysis: RR 0.89 (95% CI, 0.83-0.94; p<0.01) I2=14.1%, p=0.319
- Subgroup analysis (cross sectional studies): RR 0.83 (95% CI, 0.68-1.02; p=0.078) I2=60.4%; p=0.056
- Subgroup analysis (cohort studies): RR 0.88 (95% CI, 0.79-0.96; p=0.007) I2=0%, p=0.923

Publication bias for vegetable intake: Absent
Publication bias for fruit intake: Present (Absent after one study was excluded—result still remained significant)

Limitations
- Diagnostic criteria of depression were inconsistent
- Diet assessment methods varied among the studies and dose-response relationship could not be accessed
- No causal relationship can be established as included studies are observational in design

Conclusion
Fruit and vegetables consumption was inversely associated with the risk of depression



Peers' comment
- Some studies are missed and not included in the meta-analysis
- Quality of studies was not accessed
- Some methodological insufficiency


Note:
RR: relative risk, CI: confidence interval
In this meta-analysis, RR<1 indicates lower risk of depression
Information on Statistical analysis
- Pooled measured: inverse-variance weighted mean of the logarithm of RR (95% CI) of depression for the highest versus lowest category of fruit and vegetable intake, respectively.
-Heterogeneity: I2 test (If I2 ≥ 50%, use random effect model pooling method; If I2 < 50%, fixed effect model)
- Publication bias: funnel plot & Egger regression asymmetry test.
- Subgroup analysis: cross-sectional or cohort study
- Meta-regression: to access the potentially important covariates that might exert substantial impacts on between-study heterogeneity.
- Leave-one-out sensitive analysis: to evaluate the key studies that have remarkable impact on the between-study heterogeneity

Whole Grain Intake and Mortality From All Causes, Cardiovascular Disease, and Cancer. A Meta-Analysis of Prospective Cohort Studies

Introduction
- Whole grains (WGs) are rich in dietary fiber, vitamins, minerals, phytochemicals, and other bioactive compounds that may jointly favor long-term health.
- This meta-analysis estimated WG ingredient intake in dry weight among eligible studies and explored potential dose-response relationships between WG intake and mortality.

Method
- Database: Medline, Embase, and clinicaltrials.gov, and unpublished results from National Health and Nutrition Examination Survey (NHANES) III and NHANES 1999 to 2004.
 - Search period: until Feb 2015
- Language:  No restriction
- Eligibility criteria: Prospective cohort design, the exposure included intakes of WG ingredients (with specified methods for calculation) or WG foods (with specified food items); and the outcome included mortality from all causes, CVD, or cancer.

Results
- Number of Studies included: 14; with 786,076 participants. Among the participants, there were 97,867 (12.45%) total death, 23,957 (3.05%) CVD death and 37,492 (4.77%) Cancer death.
- Locations of included studies: US (n=10), Scandinavian countries (n=3), UK (n=1)
- Publication bias: No

WG intake (high vs low):
-Total mortality: RR 0.84 (95% CI, 0.80-0.88; p<0.001); I2=74%; p<0.001
-CVD mortality: RR 0.82 (95% CI, 0.79-0.85; p<0.001); I2=0%; p=0.53
-Cancer mortality: RR 0.88 (95% CI, 0.83-0.94; p<0.001); I2=54%; p=0.02

For each 16-g/day increase in WG (approximately 1 serving/day), relative risk for:
- Total mortality: 0.93 (95% CI 0.92-0.94; p<0.001)
- CVD mortality: 0.91 (95% CI, 0.90-0.93; p<0.001)
- Cancer mortality: 0.95 (95% CI, 0.94-0.96; p<0.001)

- Heterogeneities were not explained by study location, WG assessments, dietary questionnaires, study aim, sample size, median follow-up duration, and adjustment for dietary factors, study quality or ages at baseline. Meta-regression unable to identify source of heterogeneity.
- Publication bias: Not detected

Conclusion:
This finding support that dietary recommendation of at least 3 servings per days of WG intake.


Note:
RR: relative risk, CI: confidence interval, I2: heterogeneity
In this meta-analysis, RR<1 indicates protective effect (lower mortality).
Heterogeneity (as indicated by I2>50% or p for I2<0.05) may weaken the findings of a meta-analysis