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 ≥95^th percentile or BMI ≥85^th 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 <50^th or <25^th 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

Wu et al. Obes Res Clin Pract (2016), http://dx.doi.org/10.1016/j.orcp.2016.05.005

Note:

Hirshkowitz et al. National Sleep Foundation’s sleep timeduration recommendations: methodology and results summary. Sleep Health 2015; 1(1):40-43

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

Nutrition-related health effects of organic foods: a systematic review

Introduction

- Organic foodstuffs are produced according to specified standards that emphasize the protection of the environment and control of the use of chemicals in crop production and medicines in animal production.

- A recent systematic review of peer-reviewed evidence published in the past 50 y concluded that organically and conventionally produced foodstuffs are broadly comparable in their nutrient content

- Aim: to assess the strength of evidence of the nutrition-related benefits to human health of consumption of organic foodstuffs

- This systematic review did not address: potential health effects of differences in contaminant content (such as herbicide, pesticide, and fungicide residues) of organically and conventionally produced foodstuffs or of potential wider environmental or occupational health effects of different agricultural practices.

Method

- Search period: from 1 January 1958 to 15 September 2018 (A systematic review until 10 March 2010 was done before publication)

- Database: PubMed, ISI Web of Science, CAB Abstracts, Embase (gray literature were not included)

- Language: At least English language abstract

- Inclusion criteria: Exposure to foodstuffs from organic (reported as organic, ecologic and bioorganic) or conventional (reported as conventional and intensive) farming system

- Exclusion criteria: concerned with occupational health outcomes unrelated to consumption of organic or conventional foodstuffs, investigated animal health from an exclusively veterinary perspective, or concerned with the effects of nonnutrient contaminant content (eg, chemical residues and heavy metals),

- Study type included:

i) Randomized and nonrandomized controlled trials and studies with cohort, case-control, and crosss ectional designs

ii) In vitro and ex vivo studies in human or animal cell lines and serum used to investigate human-related cell mechanisms;

iii) Animal studies that were explicit models of physiologic, biochemical, or other processes in humans

- Study quality, assessed by 4 a priori criteria (Satisfactory quality if met all criteria):

i) Clear definition of the organic production methods for the food stuffs (including name of the organic certification body)

ii) A statement on the nature (ie, type, amount, or proportion) of the organic component of the foodstuff or diet under investigation,

iii) A clear definition of the health outcome and how it was measured

iv) A statement of the statistical methods used for data analyses

- Data analyses: no meta-analysis was performed because of the marked heterogeneity of the included studies

- Result synthesis: according to 1) study hypothesis, 2) study design, 3) exposure, and 4) health outcome

Results:

- Number of studies included: 12 (8 human in vivo studies, 3 human in vitro studies, 1 animal study)

- Quality: 4/12 studies (33%) met the predefined quality criteria

1) Study Hypothesis

- 8/12 studies (67%) hypothesized that organic production methods result in higher nutrient concentrations in foodstuffs and that these compositional differences would result in different health responses

- Remaining 4 studies hypothesized that agricultural production methods differentially affect markers of carcinogenesis or the bioavailability of carotenoids or polyphenolic substances

2) Study designs

- 8 human studies: 6 clinical trials, 1 cohort and 1 cross-sectional study

- 4 experiments conducted in animals (rats) or human cell lines or serum

- Clinical trials: small (sample size, n=6-43) and short (exposure period 1-28 d)

- Cross-sectional and cohort: larger size (n=312-2764), both derived from KOALA Birth Cohort Study. Design shortcomings: self- or proxy-reported measures of exposure, a lack of information on the duration of exposure, and proxy-reported measures of primary outcomes

- Remaining 4 experiment studies used contrasting approaches to test different biological materials in animal or human samples

3) Exposures

- 10 studies investigated the effects of specific foodstuffs (fruit or vegetables, n=7; wine, n=2; livestock products, n=1)

- 9 studies investigated foodstuffs known to be rich in antioxidants such as tomatoes, grapes, apples, carrots, and strawberries

- 2 studies investigated organic foodstuffs as part of the whole diet

- 10 studies compared a specified exposure or dose of a foodstuff as part of a controlled-exposure study

- The 2 large studies used measures of self- or proxy-reported exposure and proxy reports of diet when necessary (16% of study participants)

4) Health outcomes

- 10 studies: primary outcome was the change in antioxidant activity (Antioxidant status and activity are useful biomarkers but do not directly equate to a health outcome)

- 1 study recorded proxy-reported measures of atopic manifestations as its primary health outcome

- 1 study examined the fatty acid composition of breast milk and implied possible health benefits for infants from the consumption of different amounts of conjugated linoleic acids from breast milk

Discussion:

- Evidence of nutrition-related health effects from the consumption of organic food is currently lacking.

- The strength of evidence of other public and environmental health benefits that arises from the consumption of organic foods would warrant further systematic review.

-Clinical trials included: small sample size, none reported power calculation, inadequate reporting etc

- Most studies investigated the health effects of specific foodstuffs rather than the diet as a whole, and there was rarely any rationale provided for the quantity and duration of exposure to foodstuffs in clinical trials

- A surprising and important finding of this review is the extremely limited nature of the evidence base on this subject, both in terms of the number and quality of studies. This is particularly surprising given the increasing public and policy-level interest in the question of whether there are health benefits from the consumption of organic foods.

Conclusion:

- Evidence is lacking

- Taken together, the 12 included articles did not provide evidence of health benefits or harm from consuming organic foods

Future studies:

- The International Research Association for Organic Food Quality and Health aims to develop novel methods to study the effect of organic food on human health and has recently provided some useful guidelines on study design

- Recommendation for future studies: sufficient sample size, longer and more realistic dietary exposures, and more accurate and objective approaches to measure dietary intake and outcomes of public health relevance

Dangour et al. Am J Clin Nutr 2010; 92:203-10. doi: 10.3945/ajcn.2010.29269

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