Study

Participants at baseline

+ / 0 / ‐

Results and/or estimate of effect?

CARDIA Ludwig 1999 (1)

USA

2909 healthy black and white young adults

Baseline age: 18 to 30 yrs

Follow‐up: 10 yrs

%E from fat: unclear (lower quintile < 30, upper > 41.7)

BMI: unclear

+ (weight) in black men and women

0 (weight) in white men and women

Adjusted means of 10‐year body weight according to quintiles of total fat as a percentage of total energy. P for trend 0.32 in white men and women (quintile 1 weight 168.6 lb, quintile 5 weight 169.4 lb), 0.03 for black men and women (quintile 1 weight 182.1 lb, quintile 5 weight 185.7 lb)

Danish Diet Cancer & Health Study Halkjaer 2009 (2‐4)

Denmark

22,570 women and 20,126 men

Baseline age: 50 to 64 yrs

Follow‐up: 5 yrs

%E from fat: unclear (approx 32% in women, 33% in men)

BMI: median 24.7 women, 26.1 men

0 (Δ waist) women

0 (Δ waist) men

Association between total fat intake at baseline and change in waist circumference over 5 years suggested no statistically significant effects in women (mean change in waist circumference ‐0.03 cm/MJ/d total fat, 95% CI ‐0.20 to 0.14) or men (mean change in waist circumference 0.06 cm/MJ/d total fat, 95% CI ‐0.05 to 0.17)

12,353 women and 10,080 men

Baseline age: 50 to 60 yrs

Follow‐up: 5 yrs

%E from fat: median 33.8% women, 35.2% in men

BMI: median 24.4 women, 25.8 men

0 (Δ waist circumference)

0 (Δ body weight)

Macronutrient energy substitution where energy from protein was replaced by fat or carbohydrate. Multiple linear regression investigated the association between dietary protein in relation to change in body weight or waist circumference over 5 years. No statistically significant effect of replacing 5%E from fat with protein on change in body weight (8.0 g/year, 95% CI ‐16.6 to 32.5, P value = 0.525) or waist circumference (0.1 mm/year, 95% CI ‐0.3 to 0.4, P value = 0.799)

Danish MONICA Iqbal 2006 (5)

Denmark

900 women and 862 men

Baseline age: 30 to 60 yrs

Follow‐up: 5 yrs

%E from fat: 43.8% (SD 6.5 women, 42.7 (SD 6.3) men

BMI: 23.4 (SD 3.7 women, 25.1 (SD 3.3) men

0 (Δ weight) women

0 (Δ weight) men

Regression assessment of total fat as %E and other dietary factors as a function of change in body weight suggested no significant effects of %E from fat on 5‐year change in body weight in women (unadjusted beta 0.47, SE 0.89, P value = 0.60, adjusted beta 0.86, SE 0.92, P value = 0.35) or men (unadjusted beta ‐0.14, SE 0.69, P value = 0.84, adjusted beta 0.11, SE 0.69, P value = 0.87)

Diabetes Control & Complications Trial (DCCT) & EDIC

Cundiff 2012 (6)

USA

1055 women and men with diabetes, HbA1c ≤ 9.5

Baseline age: 13 to 39 yrs (mean 27.4)

Follow‐up: 14 to 19 yrs (mean 16.4 yrs)

%E from fat: 36.2% (90% CI 26.6 to 45.1)

BMI: 23.4 (90% CI 19.4 to 27.9)

0 (Δ BMI/year)

Multiple regression analyses generated the formula linking macronutrient intake and exercise at baseline with change in BMI per year. Univariate analyses suggested no relationship between total fat (as %E) and change in BMI per year (β 0.04 kg/m²/year, P value = 0.22), and only total fat minus polyunsaturated fat (%E, not total fat) was included in the formula predicting BMI change per year

EPIC‐PANACEA

Vergnaud 2013 (7)

Europe (10 countries)

EPIC

Beulens 2014 (8)

Europe (15 cohorts)

373,803 men and women from the general European population

Baseline age: 25 to 70 yrs

Follow‐up: 5 yrs (2 to 11)

%E from fat: mean 35.4 (SD unclear)

BMI: mean 25.6 women, 26.7 men (SDs unclear)

0 (Δ weight) when replacing fat with CHO in women or men

‐ (Δ weight) when replacing fat with protein in women or men

Multivariate substitution models were performed to estimate weight change associated with replacement of 5%E of one macronutrient with another. 5% greater proportion of E from fat at the expense of carbohydrate was not associated with weight change in women or men (P value = 0.36, P value = 0.73). Replacing 5%E from protein with fat was associated with weight reduction in women (β 0.4 kg/5 years, P value < 0.0001) and men (β 0.3 kg/5 years, P value = 0.003)

6192 people with type 2 diabetes

Baseline age: unclear

Follow‐up: 5 yrs

%E from fat: unclear

BMI: unclear

‐ (Δ weight) when replacing CHO with total fat

Linear regression was used to explore the relationship between replacement of CHO with total fat (and also MUFA and PUFA) and 5‐year weight change. This is an abstract so results reported as "5‐year weight change decreased when carbohydrates were substituted with total fat" (no further details)

Health Professionals Follow‐Up Study (HPFUS)

Coakley 1998 (9)

USA

19,478 male health professionals

Baseline age: 45 to 75 yrs

Follow‐up: 4 yrs

%E from fat: unclear, energy adjusted fat intake mean 69.6 g/d (SD 13.8)

BMI: unclear

+ (Δ weight) 45 to 54 yrs men

+ (Δ weight) 55 to 64 yrs men

0 (Δ weight) 65+ yrs men

Multivariate regression analyses determined whether total fat intake and other habits were predictive of 4‐year weight change, and found that a change of adjusted fat intake of 10 g/d predicted 0.10 kg of weight change over 4 years (P value < 0.001 for ages 45 to 54 and 55 to 64 years, P value > 0.05 for age 65+)

Melbourne Collaborative Cohort Study (MCCS)

MacInnis 2013 (10)

Australia

5879 healthy Australian‐born non‐smokers

Baseline age: 40 to 69 yrs

Follow‐up: 11.7 yrs

%E from fat: 33% (SD 6) women, 33 (SD 5) men

BMI: unclear

+ (weight) overall

+ (waist circumference) overall

+ (weight) 40 to 49 yrs

0 (weight) 50 to 59 yrs

0 (weight) 60 to 69 yrs

+ (waist) 40 to 49 yrs

+ (waist) 50 to 59 yrs

0 (waist) 60 to 69 yrs

Multivariable linear regression was used to predict waist circumference and weight at 12‐year follow‐up. Higher percentage of energy from fat at baseline was associated with weight (0.26 kg per 10%E from fat, P value = 0.03) and waist circumference (0.85 cm per 10%E from fat, P value < 0.001) in the whole sample. When assessed in age bands, total fat was associated with weight in those aged 40 to 49 years at baseline (P value = 0.002), but not in those aged 50 to 59 (P value = 0.94) or 60 to 69 years (P value = 0.79), and with waist circumference in those aged 40 to 49 (P value < 0.001) and 50 to 59 (P value = 0.01), but not in those aged 60 to 69 (P value = 0.14)

Memphis

Klesges 1992 (11‐13)

USA

152 women and 142 men (Caucasian health professionals)

Baseline age: 24 to 52 yrs

Follow‐up: 2 yrs

%E from fat: mean 36.8 (SD 6.1) women, 36.0 (SD 5.4) men

BMI: mean 24.8 (SD 5.0) women, 27.8 (SD 4.3) men

+ (Δ weight) women

0 (Δ weight) men

0 (Δ waist) women

‐ (Δ waist) men

Stepwise multivariate regression analyses assessed whether various lifestyle factors were predictive of weight change over 2 years. Percentage of energy as fat was predictive of weight change in women (coefficient 0.53, SE 0.16, P value = 0.0010) but not in men (exact data not provided)

Hierarchical linear regression assessed the effects of lifestyle factors on change in waist circumference over 2 years, and found no significant effect in women (coefficient ‐0.04, P value = 0.50) but a statistically significant negative relationship in men (coefficient ‐0.05, P value = 0.04)

NHANES Follow‐up

Kant 1995 (14)

USA

4567 women and 2580 men

Baseline age: 25 to 74 yrs

Follow‐up: mean 10.6 (SD 5) yrs

%E from fat: mean 36.4 (SD 5.0) women, 37.0 (SD 10.1) men

BMI: mean 25.2 (SD 5.0) women, 25.9 (SD 5.0) men

+ (Δ weight) < 50 yrs women

0 (Δ weight) 50+ yrs women

0 (Δ weight) < 50 yrs men

0 (Δ weight) 50+ yrs men

Univariate regression analyses assessed whether fat as %E is predictive of 10‐year weight change and found no significant effects in women (Beta ‐0.011, SE 0.017, P value = 0.51) or men (Beta 0.043, SE 0.022, P value = 0.06). Effects were similar in multivariate regression in women (Beta ‐0.033, SE 0.019, P value = 0.08 for women overall, Beta ‐0.053, SE 0.025, P value = 0.04 for women aged < 50 yrs, Beta ‐0.019, SE 0.030, P value = 0.55 for women aged 50+) or men (Beta 0.021, SE 0.022, P value = 0.33 for men overall, Beta ‐0.004, SE 0.028, P value = 0.88 for men aged < 50 yrs, Beta ‐0.058, SE 0.035, P value = 0.10 for men aged 50+)

Nurses' Health Study

Colditz 1990 (15)

Field 2007 (16)

USA

31,940 women (nurses)

Baseline age: 30 to 55+

Follow‐up: 8 yrs

%E from fat: unclear

BMI: unclear

0 (Δ weight) women

Correlation between total fat (g/d) and weight gain over subsequent 4 years (beta ‐0.0007, t ‐0.4), not statistically significant

41,518 women (nurses)

Baseline age: 41 to 68 yrs (mean 53.7, SD 7.1 yrs)

Follow‐up: 8 yrs

%E from fat: 32.8 (SD 5.6)

BMI: 25.0 (SD 4.5)

? unclear (Δ weight) women

Association between a 1% difference in total fat as %E and weight change (in pounds over 8 years) was modelled using linear regression. There was a weak relationship between total fat and weight change (β 0.11 lb/1% total fat difference, P value < 0.0001 stated in text, but no statistical significance indicated in table)

Pawtucket HHP

Parker 1997 (17)

USA

289 women and 176 men

Baseline age: 18 to 64 yrs

Follow‐up: 4 yrs

%E from fat: unclear

BMI: mean 26.5 (SD 5.0)

0 (Δ weight) women and men

Multiple regression assessed association of weight change with different nutrients at baseline. Found no effect of total fat in grams on weight change over 4 years (coefficient 2.30, P value = 0.71)

San Luis Valley Diabetes Study (SLVDS)

Mosca 2004 (18)

USA

433 women and 349 men ‐ non‐diabetic, Hispanic and non‐Hispanic white

Baseline age: 20 to 74 yrs

Follow‐up: 14 yrs

%E from fat: mean 38.3 (SD 8.9) white women, 37.2 (8.9) Hispanic women, 38.9 (8.7) white men, 37.8 (9.8) Hispanic men

BMI: mean 24.3 (SD 4.4) white women, 25.0 (4.6) Hispanic women, 25.7 (3.3) white men, 24.7 (3.8) Hispanic men

+ (Δ weight) overall (includes women and men, Hispanic and non‐Hispanic white)

Linear mixed model (random‐effects, PROC MIXED in SAS) was used to assess whether those who generally consume a relatively high fat diet gain more weight over time. They found a significant association between %E from total fat and weight change between participants (β 0.012, P value = 0.0178) after adjusting for potential confounders

SEASONS

Ma 2005 (19)

USA

275 healthy women and 297 healthy men

Baseline age: 20 to 70 yrs

Follow‐up: 1 yr

%E from fat: mean 36.7 (SD 9.0)

BMI: mean 27.4 (SD 5.5)

0 (BMI) women and men – with no energy adjustment

Regression analyses to assess effects of total fat %E on BMI. Longitudinal effect was not statistically significant (coefficient 0.005, P value = 0.07)

Women’s Gothenburg

Lissner 1997 (20)

Sweden

361 women

Baseline age: 38 to 60 yrs

Follow‐up: 6 yrs

%E from fat: mean 34.1 (SD 4.0) lower fat group, 42.3 (SD 3.0) higher fat group

BMI: mean 24.6 (SD 4.1) lower fat group, 24.1 (SD 4.1) higher fat group

+ (Δ weight) sedentary

0 (Δ weight) moderate

0 (Δ weight) active

Multivariate regression used to test for interactive effects of dietary fat intake on weight change over 6 years. A significant effect of high vs low %E from fat was found in sedentary women (high fat women gained 2.64 kg while low fat women lost 0.64 kg over 6 years, P value = 0.03) but this was lost with further energy adjustment. No effects were seen in more active women (2 categories), where those with low and high fat intakes all gained 1 to 2 kg on average

Study

Participants at baseline

+ / 0 / ‐

Results and/or estimate of effect

Adelaide Nutrition Study

Magarey 2001 (1)

Australia

243 boys and girls

Age: diet analysed at 2, 4, 6, 8, 11, 13 and 15 years old

Follow‐up: assessed for each gap (e.g. 2 to 4 years, 2 to 6 years, 2 to 8 years, 4 to 6 years etc), 2 to 13 years

%E from fat: boys aged 2 yrs 38.4 (SD 5.8), girls aged 2 38.1 (SD 13.4), boys aged 15 33.2 (SD 5.6), girls aged 15 yrs 34.4 (SD 5.6)

BMI: boys aged 2 yrs 16.8 (SD 1.7), girls aged 2 16.5 (SD 1.4), boys aged 15 20.2 (SD 2.6), girls aged 15 yrs 21.4 (SD 4.1)

0 (BMI) for 20 of 21 possible age gaps

0 (triceps skinfold) for 21 of 21 possible age gaps

0 (sub‐scapular skinfold) for 20 of 21 possible age gaps

Single dietary assessment for each of 21 analyses

Analysis: multiple regression analysis was used to predict whether body fatness at a specific age was predicted by macronutrient intake at previous ages. For BMI only one of 21 possible gaps showed a statistically significant relationship between total fat intake as a percentage of energy and later BMI (a significant relationship, P value < 0.01, was only seen between fat at age 6 and BMI at age 8). For triceps skinfold none of 21 possible gaps showed a statistically significant relationship between total fat intake as a percentage of energy and later triceps skinfold. For subscapular skinfold only one of 21 possible gaps showed a statistically significant relationship between total fat intake as a percentage of energy and later sub‐scapular skinfold (a significant relationship, P value < 0.01, was only seen between fat at age 2 and skinfold at age 15)

Amsterdam Growth & Health Long. Study (AGAHLS)

Twisk 1998, Koppes 2009 (2;3)

Netherlands

83 boys (then men) and 98 girls (then women)

Age: recruited aged 13, diet analysed at ages 13, 14, 15, 16, 21, 27

Follow‐up: 14 yrs (age 27)

%E from fat: not reported

BMI: boys aged 13 yrs 17.3 (SD 1.6), girls 18.1 (SD 2.1), men aged 27 yrs 22.6 (SD 2.2), women 21.9 (SD 2.5)

0 (sum of 4 skinfolds)

0 (BMI)

Both for absolute fat intake and %E from fat

Multiple dietary assessments

Analysis: first order auto‐regressive model (fatness at each time point related to exposure at the previous time point) estimated by generalised estimating equations. There was no relationship between total fat intake (absolute, g/d) and later fatness as assessed by sum of four skinfolds (P value = 0.41) or BMI (P value = 0.23), or between fat intake as %E and later fatness as assessed by sum of four skinfolds (P value = 0.92) or BMI (P value = 0.69)

168 boys (then men) and 182 girls (then women)

Age: recruited aged 13 (SD 0.7), diet analysed at ages 13, 14, 15, 16, 21, 27, 32, 36

Follow‐up: 23 yrs (age 36)

%E from fat: not reported

BMI: as above

0 (high %body fat at age 36), 0 of 14 analyses

0 (% body fatness) in men or women

Multiple dietary assessments

Analysis: generalised estimating equation regression analyses found that dietary fat intake (%E) at ages 13, 14, 15, 16, 21, 27 or 32 did not predict high body fatness (> 25% for men, > 35% for women, assessed by DEXA at 36 years) in either men or women (in any of 7 analyses in men or 7 in women). Regression coefficients using all available data gathered between ages 13 and 36 found no relationship between %E from fat and sum of skinfolds in either men (P value = 0.42) or women (P value = 0.89)

Bogaert 2003 (4)

Australia

29 boys and 30 girls

Age: recruited aged 6 to 9 yrs, mean 8.6 (SE 0.2) yrs

Follow‐up: at 6 and 12 mo

%E from fat: 33.5 (SD 0.8) in boys aged < 8 yrs, 31.7 (SD 2.7) girls < 8 yrs, 37.5 (SD 1.2) boys aged 8+ yrs, 33.6 (SD 1.7) girls aged 8+ yrs

BMI: z scores boys mean 0.3 (SE 0.1), girls mean 0.5 (SE 0.3)

0 (Δ BMI)

Single dietary assessment

Analysis: correlations were calculated to assess the relation between %E from fat at baseline and BMI z‐score change from baseline to 12 months. No "positive relation" was found

Carruth and Skinner 2001 (5;6)

USA

29 white boys and 24 girls

Age: recruited at 24 months, diet assessed at 24 to 32, 28 to 36, 42, 48, 54, 60 months old

Follow‐up: body fat assessed at 70 months

%E from fat: 31% boys, 32% girls at 27 months, 31% boys, 33% girls at 60 months

BMI: 15.7 (SD 1.2) in boys and 15.4 (SD 1.0) in girls at 60 months

+ (%body fat)

+ (g body fat)

Multiple dietary assessments

Analysis: regression analyses (general linear models) of total fat intake (averaging over 6 dietary assessments aged 27 to 60 months) predicted body fat at 70 months (assessed as %body fat, P value = 0.02 and grams of body fat, P value = 0.01, both assessed by DEXA)

37 white boys and 33 girls

Age: recruited at 24 months (except 2 joined at 1 year, 6 joined at 2 years from similar study), diet assessed at 2.0, 2.3, 2.7, 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 7.0, 8.0 yrs old

Follow‐up: BMI assessed at 8 yrs

%E from fat: mean 32% (SD not stated)

BMI: 16.5 in boys and 16.2 in girls at 2 yrs, 16.8 in boys and 17.1 in girls at 8 yrs

+ (BMI) by g/d of fat

+ (BMI) by %E from fat

Multiple dietary assessments

Analysis: forward stepwise regression was used to assess the relationship between dietary fat (averaged from 9 sets of 3‐day dietary data from ages 2 to 8) and BMI at age 8 years. Whether assessing fat as g/d (P value = 0.004) or %E from fat (P value = 0.010) there was a significant relationship (adjusted for BMI at 2 years and adiposity rebound age)

Davison 2001 (7)

USA

197 non‐Hispanic white girls

Age: 5.4 (0.4) yrs

Follow‐up: 2 yrs (age 7.3 ±0.3)

%E from fat: 31 (SD unclear)

BMI: 15.8 (1.4)

+ (Δ BMI)

Single dietary assessment

Analysis: in hierarchical regression models, girls' fat intake (as %E) at 5 yrs had a significant relationship with change in BMI from 5 to 7 years, P value = 0.02

Etude Longitud. Alimentation Nutrition Croissance des Enfants (ELANCE)

Rolland‐Cachera 2013 (8)

France

40 boys and 33 girls whose diets were assessed at 2 yrs

Age: 2 yrs

Follow‐up: 18 years (age 20)

%E from fat: 31.9 (SD 5.7) boys, 32.8 (SD 4.5) girls

BMI: unclear

0 (BMI)

0 (% triceps skinfold)

‐ (% sub‐scapular skinfold)

‐ (fat mass)

Single dietary assessment (for this analysis)

Analysis: association between dietary intake at 2 years and adult body composition was analysed using linear regression models. No statistically significant relationships were found between %E from fat at 2 years and BMI (P value = 0.23), % triceps skinfold (P value = 0.19), or fat‐free mass (P value = 0.98) at age 20. Greater total fat intake predicted lower % subscapular skinfold (P value = 0.03) and fat mass (P value = 0.04). All data presented from the adjusted models

European Youth Heart Study

Brixval 2009 (9)

Denmark

171 girls and 137 boys (but total of 384 stated also, numbers vary between tables)

Age: boys 9.7 (SD 0.4) yrs, girls 9.6 (SD0.4) yrs

Follow‐up: 6 years (age 15 to 16)

%E from fat: 32.1 (SD 6.6) boys, 33.3 (SD 6.7) girls

BMI: 17.1 (SD 2.0) boys, 17.2 (SD 2.4) girls

0 (Δ BMI z‐score) boys

0 (Δ BMI z‐score) girls

Single dietary assessment.

Analysis: examined the associations between dietary fat intake at 9 years and subsequent 6‐year weight development using regression analysis. None of the regression models (various levels of adjustment) suggested that fat %E was associated with change in BMI over 6 years (in boys P value = 0.27, girls P value = 0.75 in the most adjusted model)

Klesges 1995 (10)

USA

110 boys and 93 girls

Age: 3 to 5 yrs (boys 4.4 (0.5), girls 4.3 (0.5)

Follow‐up: 2 yrs

%E from fat: boys and girls 33.0 (5.0)

BMI: boys 16.1 (1.4), girls 16.1 (1.2)

0 /+ /0/0 (Δ BMI)

Multiple dietary assessments

Analysis: assessed whether baseline %E from fat, change from baseline to 1 year, 1 yr to 2 yrs, or baseline to 2 yrs (along with other variables) predicted change in BMI over 2 yrs

Multiple regression analysis suggested lower baseline %E from fat correlated to lower BMI change (regression coefficient = 0.034, P value = 0.05 – marginal significance) at 2 yrs, 0.17 k/m²per 5% more E from fat

Change in %E from fat over the last year was correlated with BMI change (regression numbers not legible, probably P value = 0.01), 0.20 kg/m² per 5%E from fat change.

Change in %E from fat from baseline to 1 yr, and baseline to 2 yrs did not predict change in BMI

Obesity & Metabolic Disorders Cohort in Children (OMDCC)

Lee 2012 (11)

Korea

1504 1st and 4th grade children

Age: 7.3 (SD 0.3) in 1st graders, 10.0 (SD 0.4) years in 4th graders

Follow‐up: 2 years

%E from fat: 26.6 (SD 4.9) in 1st graders, 25.2 (SD 5.1) in 4th graders

BMI: 16.0 (SD 2.3) in 1st graders, 18.1 (SD 3.0) in 4th graders

0 (Δ BMI)

Single dietary assessment

Multiple linear regression modelling assessed relationships between baseline environmental factors, parental and lifestyle habits and change in BMI over 2 years. They found no statistically significant relationship between fat intake and change in BMI over 2 years (P value = 0.104)

Trial of Activity for Adolescent Girls (TAAG)

Cohen 2014 (12)

USA

265 girls in 8th grade

Age: mean 13.9 (SD 0.4) yrs

Follow‐up: 2 and 3 yrs

%E from fat: unclear

BMI: mean 22.1 (SD 5.2)

0 (BMI percentile)

‐ (% body fat)

Single dietary assessment

Multivariable random coefficients model designed to examine whether habitual physical activity, diet and environmental exposure were predictive of future weight gain or percentage body fat. The multivariate model found no relationship between fat calories at baseline and BMI percentile (P value = 0.16), but suggested a reduction in % body fat associated with increased fat calories (P value = 0.03)

Viva la Familia Study

Butte 2007 (13)

USA

1030 Hispanic boys and girls (unclear how many of each)

Age: unclear, 4 to 19 yrs?

Follow‐up: 1 yr

%E from fat: 34.0 (6.0)

BMI: not stated

+

(Δ weight)

Single dietary assessment

Analysis: %E from fat was positively correlated with 1 yr weight gain (kg/y).

For 798 participants generalised estimating equations (GEE) suggested coefficient 0.044, SD 0.018, P value = 0.014

Study

Reason for exclusion

Alexy U, Reinehr T, et al. (2006). Positive changes of dietary habits after an outpatient training program for overweight children. Nutrition Research 26(5): 202‐8

Weight loss intention

Amesz EMS. Optimal growth and lower fat mass in preterm infants fed a protein‐enriched postdischarge formula. Journal of Pediatric Gastroenterology and Nutrition. 2010;50(2):200‐7

Includes infants

Anand SS, Davis AD, et al. (2007). A family‐based intervention to promote healthy lifestyles in an aboriginal community in Canada. Canadian Journal of Public Health Revue Canadienne de Sante Publique. 98(6): 447‐52

Weight loss intention

Angelopoulos PD, Milionis HJ, et al. (2009). Changes in BMI and blood pressure after a school based intervention: the CHILDREN study. European Journal of Public Health 19(3): 319‐25

Multifactorial intervention

Burrows TJ. Long‐term changes in food consumption trends in overweight children in the HIKCUPS intervention. Journal of Pediatric Gastroenterology and Nutrition. 2011;53(5):543‐7

All obese or overweight at baseline

Dal Molin Netto B, Landi Masquio DC, Da Silveira Campos RM, De Lima Sanches P, Campos Corgosinho F, Tock L, et al. The high glycemic index diet was an independent predictor to explain changes in agouti‐related protein in obese adolescents. Nutricion Hospitalaria. 2014;29(2):305‐14

Obese adolescents

Evans RK, Franco RL, et al. (2009). Evaluation of a 6‐month multi‐disciplinary healthy weight management program targeting urban, overweight adolescents: effects on physical fitness, physical activity, and blood lipid profiles. International Journal of Pediatric Obesity 4(3): 130‐3

Multifactorial intervention, weight loss goal

Forneris T, Fries E, et al. (2010). Results of a rural school‐based peer‐led intervention for youth: goals for health. Journal of School Health 80(2): 57‐65

No relevant outcomes

Garnett SPB. Researching Effective Strategies to Improve Insulin Sensitivity in Children and Teenagers ‐ RESIST. A randomised control trial investigating the effects of two different diets on insulin sensitivity in young people with insulin resistance and/or pre‐diabetes. BMC Public Health. 2010;10(pp 575):2010. 2. Garnett SPD. Optimum macronutrient content of the diet for adolescents with pre‐diabetes; RESIST a randomised control trial ACTRN12608000416392. Endocrine Reviews. 2012;Conference(var.pagings)

All obese or overweight at baseline

Hernandez TLA. Women with gestational diabetes randomised to a low‐carbohydrate/higher fat diet demonstrate greater insulin resistance and infant adiposity. Diabetes. 2013;Conference(var.pagings):July

Effect on infants

Horan MKM. The association of maternal characteristics and macronutrient intake in pregnancy with neonatal body composition. Archives of Disease in Childhood: Fetal and Neonatal Edition. 2014;Conference(var.pagings):June

Infants

Jebb SA, Frost G, et al. (2007). The RISCK study: Testing the impact of the amount and type of dietary fat and carbohydrate on metabolic risk. Nutrition Bulletin 32(2): 154‐6

Design paper

Kaitosaari T, Ronnemaa T, et al. (2006). Low‐saturated fat dietary counselling starting in infancy improves insulin sensitivity in 9‐year‐old healthy children: the Special Turku Coronary Risk Factor Intervention Project for Children (STRIP) study. Diabetes Care 29(4): 781‐5

No relevant outcomes

Lagstrom H, Hakanen M, et al. (2008) Growth patterns and obesity development in overweight or normal‐weight 13‐year‐old adolescents: the STRIP study. Pediatrics 122(4): e876‐83

No relevant exposures

Mirza NM, Palmer MG, Sinclair KB, McCarter R, He J, Ebbeling CB, et al. Effects of a low glycemic load or a low‐fat dietary intervention on body weight in obese Hispanic American children and adolescents: a randomised controlled trial. American Journal of Clinical Nutrition. 2013;97(2):276‐85

All obese at baseline

Mobley CCS. Effect of nutrition changes on foods selected by students in a middle school‐based diabetes prevention intervention program: The HEALTHY experience. Journal of School Health. 2012;82(2):82‐90

No total fat intake assessment

Niinikoski H, Lagstrom H, Jokinen E, Siltala M, Ronnemaa T, Viikari J, et al. Impact of repeated dietary counselling between infancy and 14 years of age on dietary intakes and serum lipids and lipoproteins: the STRIP study. Circulation. 2007;116(9):1032‐40

Aim to reduce saturated fat not total fat

Ramon‐Krauel MS. A low‐glycemic‐load versus low‐fat diet in the treatment of fatty liver in obese children. Childhood Obesity. 2013;9(3):252‐60

All obese at baseline

Shalitin S, Ashkenazi‐Hoffnung L, et al. (2010). Effects of a twelve‐week randomised intervention of exercise and/or diet on weight loss and weight maintenance, and other metabolic parameters in obese preadolescent children. Hormone Research 72(5): 287‐301

Weight loss/unsuitable exposures

Sharma SF. One‐year change in energy and macronutrient intakes of overweight and obese inner‐city African American children: Effect of community‐based Taking Action Together type 2 diabetes prevention program. Eating Behaviors. 2012;13(3):271‐4

All obese or overweight at baseline

Singhal A, Kennedy K, Lanigan J, Fewtrell M, Cole TJ, Stephenson T, et al. Nutrition in infancy and long‐term risk of obesity: evidence from 2 randomised controlled trials. American Journal of Clinical Nutrition. 2010;92(5):1133‐44

Infants

Thakwalakwa C, Ashorn P, Phuka J, Cheung YB, Briend A, Puumalainen T, et al. A lipid‐based nutrient supplement but not corn‐soy blend modestly increases weight gain among 6‐ to 18‐month‐old moderately underweight children in rural Malawi. Journal of Nutrition 2010;140(11):2008‐13

Duration < 26 weeks

Williamson DA, Han H, Johnson WD, Martin CK, Newton RL, Jr. Modification of the school cafeteria environment can impact childhood nutrition. Results from the Wise Mind and LA Health studies. Appetite. 2013;61(1):77‐84

Weight loss aimed

Williamson DA, Copeland AL, et al. (2007). Wise Mind project: a school‐based environmental approach for preventing weight gain in children. Obesity 15(4): 906‐17

Multifactorial intervention

Study

Reason for exclusion

Adams T, Rini A (2007). Predicting 1‐year change in body mass index among college students. Journal of American College Health 55(6): 361‐5

No relevant exposures

Aerenhouts D, Deriemaeker P, Hebbelinck M, Clarys P, Aerenhouts D, Deriemaeker P, et al. Energy and macronutrient intake in adolescent sprint athletes: a follow‐up study. Journal of Sports Sciences. 2011;29(1):73‐82

No relationship between total fat and body fatness

Ahluwalia N, Ferrieres J, et al. (2009). Association of macronutrient intake patterns with being overweight in a population‐based random sample of men in France. Diabetes & Metabolism 35(2): 129‐36

Invalid study design

Aljadani HM, Patterson A, Sibbritt D, Hutchesson MJ, Jensen ME, Collins CE. Diet quality, measured by fruit and vegetable intake, predicts weight change in young women. Journal of Obesity. 2013;2013:525161

No relevant outcomes

Almoosawi S, Prynne CJ, Hardy R, Stephen AM. Time‐of‐day and nutrient composition of eating occasions: prospective association with the metabolic syndrome in the 1946 British birth cohort. International Journal of Obesity. 2013;37(5):725‐31

No total fat assessment

Al‐Sarraj T, Saadi H, et al. (2010). Metabolic syndrome prevalence, dietary intake, and cardiovascular risk profile among overweight and obese adults 18‐50 years old from the United Arab Emirates. Metabolic Syndrome & Related Disorders 8(1): 39‐46

Cross‐sectional study

Althuizen E, van Poppel MN, de Vries JH, Seidell JC, van MW, Althuizen E, et al. Postpartum behaviour as predictor of weight change from before pregnancy to one year postpartum. BMC Public Health. 2011;11:165

Total fat assessment is not baseline

Bailey BWS. Dietary predictors of visceral adiposity in overweight young adults. British Journal of Nutrition. 2010;103(12):1702‐5

Cross‐sectional

Berg CM, Lappas G, et al. (2008). Food patterns and cardiovascular disease risk factors: the Swedish INTERGENE research program. American Journal of Clinical Nutrition 88(2): 289‐97

Invalid study design

Bes‐Rastrollo M, van Dam RM, et al. (2008) Prospective study of dietary energy density and weight gain in women. American Journal of Clinical Nutrition 88(3): 769‐77

Not total fat to body fatness

Black MHW. High‐fat diet is associated with obesity‐mediated insulin resistance and beta‐cell dysfunction in Mexican Americans. Journal of Nutrition. 2013;143(4):479‐85. 2. Black MHW. Variants in PPARG interact with high‐fat diet to influence longitudinal decline in beta‐cell function in Mexican Americans at risk for type 2 diabetes (T2D). Diabetes. 2014;Conference(var.pagings):June

Not prospective

Bujnowski D, Xun P, Daviglus ML, Van HL, He K, Stamler J, et al. Longitudinal association between animal and vegetable protein intake and obesity among men in the United States: the Chicago Western Electric Study. Journal of the American Dietetic Association. 2011;111(8):1150‐5

No total fat intake assessment

Carvalho LKB. Annual variation in body fat is associated with systemic inflammation in chronic kidney disease patients Stages 3 and 4: A longitudinal study. Nephrology Dialysis Transplantation. 2012;27(4):1423‐8

No total fat assessment and chronic kidney disease

Castellanos DC, Connell C, Lee J. Factors affecting weight gain and dietary intake in Latino males residing in Mississippi: a preliminary study. Hispanic Health Care International. 2011;9(2):91‐8

Cross‐sectional

Chang A, Van Horn L, Jacobs Jr DR, Liu K, Muntner P, Newsome B, et al. Lifestyle‐related factors, obesity, and incident microalbuminuria: the CARDIA (Coronary Artery Risk Development in Young Adults) Study. American Journal of Kidney Diseases. 2013;62(2):267‐75

Assesses dietary patterns

Chopra VP. Dietary factors affecting weight gain in midlife women. FASEB Journal. 2013;Conference(var.pagings):April

All overweight or obese at baseline

de Groot S, Post MW, Snoek GJ, Schuitemaker M, van der Woude LH. Longitudinal association between lifestyle and coronary heart disease risk factors among individuals with spinal cord injury. Spinal Cord. 2013;51(4):314‐8

No total fat assessment

de Koning L, Malik VS, Kellogg MD, Rimm EB, Willett WC, Hu FB. Sweetened beverage consumption, incident coronary heart disease, and biomarkers of risk in men. Circulation. 2012;125(14):1735‐41

No body fatness outcomes

Dujmovic M, Kresic G, Mandic ML, Kenjeric D, Cvijanovic O, Dujmovic M, et al. Changes in dietary intake and body weight in lactating and non‐lactating women: prospective study in northern coastal Croatia. Collegium Antropologicum. 2014;38(1):179‐87

Follow‐up < 1 year

Eghtesadi SS‐K. Dietary patterns predicting changes in obesity indices (BMI,WC,WHR) in longitudinal Tehran lipid and glucose study. Annals of Nutrition and Metabolism. 2013;Conference(var.pagings):2013

No total fat intake assessment

Erber E, Hopping BN, Grandinetti A, Park SY, Kolonel LN, Maskarinec G. Dietary patterns and risk for diabetes: the multiethnic cohort. Diabetes Care. 2010;33(3):532‐8

No total fat intake assessment and no body fatness outcomes

Ericson U, Rukh G, Stojkovic I, Sonestedt E, Gullberg B, Wirfalt E, et al. Sex‐specific interactions between the IRS1 polymorphism and intakes of carbohydrates and fat on incident type 2 diabetes. American Journal of Clinical Nutrition. 2013;97(1):208‐16

Cross‐sectional

Hairston KGV. Lifestyle factors and 5‐year abdominal fat accumulation in a minority cohort: The IRAS family study. Obesity. 2012;20(2):421‐7

No total fat intake assessment

Heppe DHMV. Maternal milk consumption, fetal growth, and the risks of neonatal complications: The Generation R Study. American Journal of Clinical Nutrition. 2011;94(2):501‐9

Fetal growth assessment

Holmberg S, Thelin A, Holmberg S, Thelin A. High dairy fat intake related to less central obesity: a male cohort study with 12 years' follow‐up. Scandinavian Journal of Primary Health Care. 2013;31(2):89‐94

No total fat intake assessment

Ibe YT. Food groups and weight gain in Japanese men. Clinical Obesity. 2014;4(3):157‐64

No relationship between total fat and body fatness assessed

Jaacks LMG. Age, period and cohort effects on adult body mass index and overweight from 1991 to 2009 in China: The China Health And Nutrition Survey. International Journal of Epidemiology. 2013;42(3):828‐37

No total fat intake assessment

Jaakkola JH. Eating behavior influences diet, weight, and central obesity in women after pregnancy. Nutrition. 2013;29(10):1209‐13

No total fat intake assessment

Jarvandi S, Gougeon R, Bader A, Dasgupta K, Jarvandi S, Gougeon R, et al. Differences in food intake among obese and non‐obese women and men with type 2 diabetes. Journal of the American College of Nutrition. 2011;30(4):225‐32

Cross‐sectional

Johns DJ, Ambrosini GL, Jebb SA, Sjöström L, Carlsson LMS, Lindroos AK. Tracking of an energy‐dense, high saturated fat, low‐fibre dietary pattern, foods and nutrient composition over 10 years in the severely obese. Journal of Human Nutrition & Dietetics. 2011;24(4):391‐2. 2. Johns DJ, Lindroos AK, Jebb SA, Sjostrom L, Carlsson LM, Ambrosini GL, et al. Tracking of a dietary pattern and its components over 10‐years in the severely obese. PLoS One [Electronic Resource]. 2014;9(5):e97457

No relevant outcomes

Kimokoti RWG. Dietary patterns of women are associated with incident abdominal obesity but not metabolic syndrome. Journal of Nutrition. 2012;142(9):1720‐7. 2. Kimokoti RWN. Diet quality, physical activity, smoking status, and weight fluctuation are associated with weight change in women and men. Journal of Nutrition. 2010;140(7):1287‐93

No total fat intake assessment

Kirk JK, Craven T, Lipkin EW, Katula J, Pedley C, O'Connor PJ, et al. Longitudinal changes in dietary fat intake and associated changes in cardiovascular risk factors in adults with type 2 diabetes: the ACCORD trial. Diabetes Research & Clinical Practice. 2013;100(1):61‐8

Compares PEP score, not total fat

Ko GTC, Chan JCN, et al. (2007). Associations between dietary habits and risk factors for cardiovascular diseases in a Hong Kong Chinese working population‐‐the "Better Health for Better Hong Kong" (BHBHK) health promotion campaign. Asia Pacific Journal of Clinical Nutrition 16(4): 757‐65

No relevant exposures

Laatikainen T, Philpot B, Hankonen N, Sippola R, Dunbar JA, Absetz P, et al. Predicting changes in lifestyle and clinical outcomes in preventing diabetes: The Greater Green Triangle Diabetes Prevention Project. Preventive Medicine. 2012;54(2):157‐61

No relevant outcomes

Manios Y, Kourlaba G, Grammatikaki E, Androutsos O, Ioannou E, Roma‐Giannikou E, et al. Comparison of two methods for identifying dietary patterns associated with obesity in preschool children: the GENESIS study. European Journal of Clinical Nutrition. 2010;64(12):1407‐14

Cross‐sectional

Meidtner KF. Variation in genes related to hepatic lipid metabolism and changes in waist circumference and body weight. Genes and Nutrition. 2014;9(2)

No total fat intake assessment

Mejean C, Macouillard P, Castetbon K, Kesse‐Guyot E, Hercberg S, Mejean C, et al. Socio‐economic, demographic, lifestyle and health characteristics associated with consumption of fatty‐sweetened and fatty‐salted foods in middle‐aged French adults. British Journal of Nutrition. 2011;105(5):776‐86

No total fat intake assessment

Mirmiran PB. Association between dietary phytochemical index and 3‐year changes in weight, waist circumference and body adiposity index in adults: Tehran Lipid and Glucose study. Nutrition and Metabolism. 2012(9):108

No assessment of total fat on body fatness

Moran LJ, Ranasinha S, Zoungas S, McNaughton SA, Brown WJ, Teede HJ, et al. The contribution of diet, physical activity and sedentary behaviour to body mass index in women with and without polycystic ovary syndrome. Human Reproduction. 2013;28(8):2276‐83

Cross‐sectional

Mozaffarian D, Cao H, King IB, Lemaitre RN, Song X, Siscovick DS, et al. Circulating palmitoleic acid and risk of metabolic abnormalities and new‐onset diabetes. American Journal of Clinical Nutrition. 2010;92(6):1350‐8

No body fatness outcomes

Naniwadekar AS. Nutritional assessment of patients with chronic pancreatitis and impact of dietary advice. Gastroenterology. 2010;Conference(var.pagings):S393

Pancreatitis patients

Neeland IJT. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. JAMA ‐ Journal of the American Medical Association. 2012;308(11):1150‐9

No total fat intake assessment

Niu J, Seo DC, Niu J, Seo DC. Central obesity and hypertension in Chinese adults: a 12‐year longitudinal examination. Preventive Medicine. 2014;62:113‐8

No relevant outcomes

Noori N, Dukkipati R, Kovesdy CP, Sim JJ, Feroze U, Murali SB, et al. Dietary omega‐3 fatty acid, ratio of omega‐6 to omega‐3 intake, inflammation, and survival in long‐term hemodialysis patients. American Journal of Kidney Diseases. 2011;58(2):248‐56

No total fat assessment and haemodialysis patients

Plotnikoff RC, Karunamuni N, et al. (2009) An examination of the relationship between dietary behaviours and physical activity and obesity in adults with type 2 diabetes. Canadian Journal of Diabetes 33(1): 27‐34

No relevant exposures

Qi QR. Consumption of branched chain amino acids and risk of coronary heart disease in us men and women. Circulation. 2013;Conference(var.pagings)

No total fat intake on weight assessment

Quatromoni PA, Pencina M, Cobain MR, Jacques PF, D'Agostino RB. Dietary quality predicts adult weight gain: findings from the Framingham Offspring Study. Obesity (Silver Spring, Md). 2006;14(8):1383‐91

No relevant outcomes

Rautiainen SW. Dairy consumption and risk of becoming overweight or obese in middle‐aged and older women. Circulation. 2014;Conference(var.pagings):25

No total fat intake assessment

Rukh G, Sonestedt E, Melander O, Hedblad B, Wirfalt E, Ericson U, et al. Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmo Diet and Cancer Study. Genes & Nutrition. 2013;8(6):535‐47
2. Rukh GS. Genetic susceptibility for obesity increases the risk of type 2 diabetes and is modified by macronutrient intakes. Diabetologia. 2010;Conference(var.pagings):September
3. Rukh GS. Genetic susceptibility to obesity associates with type 2 diabetes and interacts with dietary intake to predispose for obesity. Obesity Reviews. 2010;Conference(var.pagings):July

Not prospective

Sammel MD, Grisson JA, Freeman EW, Hollander L, Liu L, Liu S, et al. Weight gain among women in the late reproductive years. Family Practice 2003; 20: 401–9

No total fat assessment

Sanchez‐Villegas A, Bes‐Rastrollo M, Martinez‐Gonzalez MA, Serra‐Majem L. Adherence to a Mediterranean dietary pattern and weight gain in a follow‐up study: the SUN cohort. International Journal of Obesity 2006; 30: 350–8

No relevant outcomes

Sayon‐Orea CB‐R. Longitudinal association between yogurt consumption and weight gain, and the risk of overweight/obesity: The SUN cohort study. Obesity Facts. 2014;Conference(var.pagings):May

No total fat intake assessment

Scholz U, Ochsner S, Hornung R, Knoll N, Scholz U, Ochsner S, et al. Does social support really help to eat a low‐fat diet? Main effects and sex differences of received social support within the Health Action Process Approach. Applied Psychology. 2013;Health and Well‐being. 5(2):270‐90

All obese or overweight at baseline

Schulz M, Kroke A, Liese AD, Hoffmann K, Bergmann MM, Boeing H. Food groups as predictors for short‐term weight changes in men and women of the EPIC Potsdam cohort. Journal of Nutrition 2002; 132: 1335–40

No total fat assessment

Sherafat‐Kazemzadeh R, Egtesadi S, Mirmiran P, Gohari M, Farahani SJ, Esfahani FH, et al. Dietary patterns by reduced rank regression predicting changes in obesity indices in a cohort study: Tehran Lipid and Glucose Study. Asia Pacific Journal of Clinical Nutrition. 2010;19(1):22‐32.2. Sherafat‐Kazemzadeh R, Egtesadi S, Mirmiran P, Hedayati M, Gohari M, Vafa M, et al. Predicting of changes in obesity indices regarding to dietary patterns in longitudinal Tehran lipid and glucose study. Iranian Journal of Endocrinology & Metabolism. 2010;12(2):197

No assessment of total fat on body fatness

Simpson A, Maynard V, Simpson A, Maynard V. A longitudinal study of the effect of Antarctic residence on energy dynamics and aerobic fitness. International Journal of Circumpolar Health. 2012;71:17227

No total fat intake assessment

Tanisawa KI. Strong influence of dietary intake and physical activity on body fatness in elderly Japanese men: age‐associated loss of polygenic resistance against obesity. Genes and Nutrition. 2014;9(5)

Cross‐sectional

Threapleton DE, Greenwood DC, Burley VJ, Aldwairji M, Cade JE, Threapleton DE, et al. Dietary fibre and cardiovascular disease mortality in the UK Women's Cohort Study. European Journal of Epidemiology. 2013;28(4):335‐46

No total fat intake assessment

Vadiveloo M, Scott M, Quatromoni P, Jacques P, Parekh N, Vadiveloo M, et al. Trends in dietary fat and high‐fat food intakes from 1991 to 2008 in the Framingham Heart Study participants. British Journal of Nutrition. 2014;111(4):724‐34. 2. Vadiveloo MS. Increases in dietary fat intake among the Framingham heart study participants: Trends from 1991‐2008. Circulation. 2012;Conference(var.pagings)

No assessment of total fat on body fatness

Verheijden MW, van der Veen JE, van Zadelhoff WM, Bakx C, Koelen MA, van den Hoogen HJ, et al. Nutrition guidance in Dutch family practice: behavioral determinants of reduction of fat consumption. American Journal of Clinical Nutrition. 2003;77(4 Suppl):1058s‐64s

No relevant outcomes

Wang HT. Longitudinal association between dairy consumption and changes of body weight and waist circumference: The Framingham Heart Study.International Journal of Obesity. 2014;38(2):299‐305

No total fat intake assessment

Wolongevicz DM, Zhu L, Pencina MJ, Kimokoti RW, Newby PK, D'Agostino RB, et al. Diet quality and obesity in women: the Framingham Nutrition Studies. British Journal of Nutrition. 2010;103(8):1223‐9

No relevant outcomes

Yadav VM. Effects of a low fat plant based diet in multiple sclerosis (MS): results of a 1‐year long randomised controlled (RC) study. Neurology. 2014;Conference(var.pagings)

Multiple sclerosis patients

Yin JQ. Maternal diet, breastfeeding and adolescent body composition: A 16‐year prospective study. European Journal of Clinical Nutrition. 2012;66(12):1329‐34

No total fat intake assessment

Yoshimura YK. Relations of nutritional intake to age, sex and body mass index in Japanese elderly patients with type2 diabetes: The Japanese Elderly Diabetes Intervention Trial. Geriatrics and Gerontology International. 2012;12(SUPPL.1):29‐40

Cross‐sectional

Younossi ZMS. Prevalence and independent predictors of non‐alcoholic fatty liver disease (NAFLD) in lean U.S population. Hepatology. 2011;Conference(var.pagings):October

NAFLD

Yuan BD. Study on transition of dietary patterns in Jiangsu province, 1989‐2009, China. FASEB Journal. 2011;Conference(var.pagings):April. 2. Yuan BD. Nutrition transition in Jiangsu, China, 1989‐2009. Annals of Nutrition and Metabolism. 2013;Conference(var.pagings):2013

No total fat intake assessment

Zamora D, Gordon‐Larsen P, Jacobs DR, Jr., Popkin BM, Zamora D, Gordon‐Larsen P, et al. Diet quality and weight gain among black and white young adults: the Coronary Artery Risk Development in Young Adults (CARDIA) Study (1985‐2005). American Journal of Clinical Nutrition. 2010;92(4):784‐93

No assessment of total fat on body fatness

Zelber‐Sagi SL. Non‐alcoholic fatty liver disease (NAFLD) independently predicts type‐2 diabetes and pre‐diabetes during a seven‐year prospective follow‐up. Journal of Hepatology. 2012;Conference(var.pagings):April

No relevant outcomes

Study

Reason for exclusion

Alexy U, Libuda L, Mersmann S, Kersting M, Alexy U, Libuda L, et al. Convenience foods in children's diet and association with dietary quality and body weight status. European Journal of Clinical Nutrition. 2011;65(2):160‐6

Not longitudinal

Ambrosini GLE. Identification of a dietary pattern prospectively associated with increased adiposity during childhood and adolescence. International Journal of Obesity (2005). 2012;36(10):1299‐305. 2.Ambrosini GLE. Tracking a dietary pattern associated with increased adiposity in childhood and adolescence. Obesity. 2014;22(2):458‐65. 3. Ambrosini GLL. An energy‐dense, high fat, low fibre dietary pattern is prospectively associated with greater adiposity in adolescent girls in the Avon longitudinal study of parents and children. Obesity Reviews. 2010;Conference(var.pagings):July

No total fat intake assessment

Barton AJ, Gilbert L, et al. (2006). Cardiovascular risk in Hispanic and non‐Hispanic preschoolers. Nursing Research 55(3): 172‐9

Cross‐sectional study

Berz JP, Singer MR, Guo X, Daniels SR, Moore LL, Berz JPB, et al. Use of a DASH food group score to predict excess weight gain in adolescent girls in the National Growth and Health Study. Archives of Pediatrics & Adolescent Medicine. 2011;165(6):540‐6

No total fat assessment

Bigornia SJL. Dairy intakes at age 10 years do not adversely affect risk of excess adiposity at 13 years. Journal of Nutrition. 2014;144(7):1081‐90

No total fat assessment

Boreham C, Twisk J, van Mechelen W, Savage M, Strain J, Cran G. Relationships between the development of biological risk factors for coronary heart disease and lifestyle parameters during adolescence: The Northern Ireland Young Hearts Project. Public Health. 1999;113(1):7‐12

No relevant outcomes

Burke V, Beilin LJ, Simmer K, Oddy WH, Blake KV, Doherty D, et al. Predictors of body mass index and associations with cardiovascular risk factors in Australian children: a prospective cohort study.International Journal of Obesity (Lond). 2005;29(1):15‐23

No baseline fat intake

Burke V, Beilin LJ, et al. (2006). Television, computer use, physical activity, diet and fatness in Australian adolescents. International Journal of Pediatric Obesity 1(4): 248‐55

Cross‐sectional study

Chaput J‐P, Tremblay A, et al. (2008). A novel interaction between dietary composition and insulin secretion: effects on weight gain in the Quebec Family Study. American Journal of Clinical Nutrition 87(2): 303‐9

No relevant exposures

Davis JN, Alexander KE, et al. Inverse relation between dietary fiber intake and visceral adiposity in overweight Latino youth. American Journal of Clinical Nutrition 2009; 90(5): 1160‐6

Unsuitable analyses

Deshmukh UJ. Growth and body composition changes in Indian undernourished children. Annals of Nutrition and Metabolism. 2013;Conference(var.pagings):2013

No relevant outcomes

Dubois L, Farmer A, et al. (2007). Regular sugar‐sweetened beverage consumption between meals increases risk of overweight among preschool‐aged children. Journal of the American Dietetic Association 107(6): 924‐34

Invalid study design

Elliott SAT. Associations of body mass index and waist circumference with: energy intake and percentage energy from macronutrients, in a cohort of Australian children. Nutrition Journal. 2011;10(1)

Cross‐sectional

Enes CC, Slater B, Enes CC, Slater B. Variation in dietary intake and physical activity pattern as predictors of change in body mass index (BMI) Z‐score among Brazilian adolescents. Revista Brasileira de Epidemiologia. 2013;16(2):493‐501

Not prospective

Faith MS, Dennison BA, et al. (2006). Fruit juice intake predicts increased adiposity gain in children from low‐income families: weight status‐by‐environment interaction. Pediatrics 118(5): 2066‐75

No relevant exposures

Frohnert BIJ. Relation between serum free fatty acids and adiposity, insulin resistance, and cardiovascular risk factors from adolescence to adulthood. Diabetes. 2013;62(9):3163‐9

No total fat assessment

Heppe DH, Kiefte‐de Jong JC, Durmus B, Moll HA, Raat H, Hofman A, et al. Parental, fetal, and infant risk factors for preschool overweight: the Generation R Study. Pediatric Research. 2013;73(1):120‐7

No total fat intake assessment

Hooley M, Skouteris H, Millar L, Hooley M, Skouteris H, Millar L. The relationship between childhood weight, dental caries and eating practices in children aged 4‐8 years in Australia, 2004‐2008. Pediatric Obesity. 2012;7(6):461‐70

No total fat intake assessment

Hopkins DS. The effect on growth of using cows milk as the main drink for infants. Annals of Nutrition and Metabolism. 2011;Conference(var.pagings):October

Infants

Huh SYR. Prospective association between milk intake and adiposity in preschool‐aged children. Journal of the American Dietetic Association. 2010;110(4):563‐70

No total fat intake assessment

Humenikova L, Gates GE (2007). Dietary intakes, physical activity, and predictors of child obesity among 4‐6th graders in the Czech Republic. Central European Journal of Public Health 15(1): 23‐8

Cross‐sectional

Isharwal S, Arya S, et al. (2008). Dietary nutrients and insulin resistance in urban Asian Indian adolescents and young adults. Annals of Nutrition & Metabolism 52(2): 145‐51

Invalid study design

Kagura J, Feeley AB, Micklesfield LK, Pettifor JM, Norris SA, Kagura J, et al. Association between infant nutrition and anthropometry, and pre‐pubertal body composition in urban South African children. Journal of Developmental Origins of Health and Disease. 2012;3(6):415‐23

No total fat intake assessment

Khalil HM. Developmental trajectories of body mass index (BMI) from birth to late childhood and their relation with paternal and child nutrients intake. Obesity Facts. 2014;Conference(var.pagings):May

No relevant outcomes

Labayen I, Ruiz JR, Ortega FB, Huybrechts I, Rodríguez G, Jiménez‐Pavón D, et al. High fat diets are associated with higher abdominal adiposity regardless of physical activity in adolescents; the HELENA study. Clinical Nutrition. 2014;33(5):859‐66

Cross‐sectional

Li SF. Dairy consumption with onset of overweight and obesity among U.S. adolescents.FASEB Journal. 2014;Conference(var.pagings)

No total fat intake assessment

Magnussen CG, Thomson R, Cleland VJ, Ukoumunne OC, Dwyer T, Venn A, et al. Factors affecting the stability of blood lipid and lipoprotein levels from youth to adulthood: evidence from the Childhood Determinants of Adult Health Study. Archives of Pediatrics & Adolescent Medicine. 2011;165(1):68‐76

No relevant outcomes

Manios Y. (2006). Design and descriptive results of the "Growth, Exercise and Nutrition Epidemiological Study in preSchoolers": The GENESIS Study. BMC Public Health 6(32)

No fat to weight relationship

Mete MS. Dietary patterns and depression in a population with high prevalence of obesity: The strong heart family study. Circulation. 2012;Conference(var.pagings)

No total fat intake assessment

Millar L, Rowland B, Nichols M, Swinburn B, Bennett C, Skouteris H, et al. Relationship between raised BMI and sugar sweetened beverage and high fat food consumption among children. Obesity. 2014;22(5):E96‐103. 2. Millar LMR. Sugar sweetened beverage and high fat food consumption are related to raised BMI z‐scores among a cohort of Australian children from 4 to 10 years of age. Obesity Facts. 2013;Conference(var.pagings):May.

No total fat assessment

Oldewage‐Theron W, Napier C, Egal A. Dietary fat intake and nutritional status indicators of primary school children in a low‐income informal settlement in the Vaal region... [corrected] [published erratum appears in S AFR J CLIN NUTR 2011; 24(3):164]. South African Journal of Clinical Nutrition. 2011;24(2):99‐104

Cross‐sectional

Pala VL. Dietary patterns and longitudinal change in body mass in European children: a follow‐up study on the IDEFICS multicenter cohort. European Journal of Clinical Nutrition. 2013;67(10):1042‐9

No total fat intake assessment

Pan A, Malik VS, Hao T, Willett WC, Mozaffarian D, Hu FB, et al. Changes in water and beverage intake and long‐term weight changes: results from three prospective cohort studies. International Journal of Obesity. 2013;37(10):1378‐85

No total fat intake assessment

Puengputtho WL. Salt intake and salt reduction in secondary school‐age students of Princess Chulabhorn's College Chiangrai (Regional science school). Annals of Nutrition and Metabolism. 2013;Conference(var.pagings):2013

No total fat intake on weight assessment

Riedel CV. Interactions of genetic and environmental risk factors with respect to body fat mass in children: Results from the ALSPAC study. Obesity. 2013;21(6):1238‐42

No total fat intake assessment

Scharf RJ, Demmer RT, Deboer MD. Longitudinal evaluation of milk type consumed and weight status in preschoolers. Archives of Disease in Childhood. 2013;98(5):335‐40

No total fat intake assessment

Serra‐Majem L, Aranceta‐Bartrina J, et al. Prevalence and determinants of obesity in Spanish children and young people. British Journal of Nutrition. 2006;96 Suppl 1: S67‐72

Cross‐sectional

Vazaiou AP. Protein intake of toddlers in Greece and its nutritional consequences. Hormone Research in Paediatrics. 2011;Conference(var.pagings):October

No assessment of total fat on body fatness

Weijs PJM. High beverage sugar as well as high animal protein intake at infancy may increase overweight risk at 8 years: a prospective longitudinal pilot study. Nutrition Journal. 2011;10(1)

Infants

Williams CL, Strobino BA. Childhood diet, overweight, and CVD risk factors: the Healthy Start project. Preventive Cardiology. 2008;11(1):11‐20

No relevant outcomes

Wosje KS, Khoury PR, Claytor RP, Copeland KA, Hornung RW, Daniels SR, et al. Dietary patterns associated with fat and bone mass in young children. American Journal of Clinical Nutrition. 2010;92(2):294‐303

No total fat intake assessment

Yin JQ. Maternal diet, breastfeeding and adolescent body composition: A 16‐year prospective study. European Journal of Clinical Nutrition. 2012;66(12):1329‐34

No total fat intake assessment

Zaki MH. Identifying obesogenic dietary factors among Egyptian obese adolescents. Annals of Nutrition and Metabolism. 2013;Conference(var.pagings):2013

No relevant outcomes

Zhang ZG. Added sugar intake and lipids profile among us adolescents: Nhanes 2005‐2010. Circulation. 2014;Conference(var.pagings):25

Cross‐sectional

Study

Number lost to follow‐up

Baseline similarity by total fat intake, funding, control groups

Adjustments (where stratified not counted as not being adjusted)*

Method of assessment

Risk of bias**

CARDIA Ludwig 1999 (1)

USA

5111 attended original screening, 3609 attended at years 1, 7 and 10, 2909 included in analysis

43% lost or not analysed

Reasons: exclusion of those who were pregnant or lactating, with diabetes, on lipid or BP medication or with extreme dietary factors

Different. Those with lower total fat intake were more likely to be women, non‐smokers, more physically active, with higher alcohol and vitamin supplement intake

Funded by: NHLBI, NIDDKD

Control group: internal

Weight was adjusted for baseline weight. Analysis adjusted for energy, sex, age, field centre, education, energy intake, physical activity, cigarette smoking, alcohol intake, vitamin supplement use.

All adjusted for

Interviewer‐ administered FFQ (700 foods)

Single (multiple dietary assessments – but appear to use baseline data only in analysis)

High

Danish Diet Cancer & Health Study Halkjaer 2009 (2‐4)

Denmark

57,043 at baseline, 44,897 re‐assessed 5 years later

21% lost or not analysed

Reasons: 1781 had died, 435 emigrated, remainder did not want to participate or did not reply

Data not reported

Unclear

Funded by: National Danish Research Foundation, DiOGenes (EU funding)

Control group: internal

BMI, energy, age, smoking, alcohol, wine, beer, spirits, sporting activity

Not adjusted for ethnicity, or socioeconomic status

192‐item semi‐quantitative FFQ checked by dietitian

Single dietary assessment used

High

57,053 at baseline, 22,433 included in 5‐year analysis.

61% lost or not analysed

Reasons: excluded aged ≥ 60 years (baseline) or ≥ 65 years (follow‐up), did not attend follow‐up, illness at baseline or during follow‐up, average weight gain or loss > 5 kg/year or waist circumference > 7 cm/year, lack of blood sample or other baseline data

Data not reported.

Unclear

Funded by: National Danish Research Foundation, DiOGenes (EU funding)

Control group: internal

Age, sex, physical activity, smoking, education, follow‐up time, fibre intake, glycaemic index, hormone treatment and baseline body weight or waist circumference (analysed as %E from fat, so adjusted for E)

Not adjusted for ethnicity

192‐item semi‐quantitative FFQ checked by dietitian

Single dietary assessment used

High

Danish MONICA Iqbal 2006 (5)

Denmark

2025 at baseline, 1762 re‐assessed 5 years later

13% lost or not analysed

Reasons: missing or very high energy or unknown history of family obesity

Data not reported

Unclear

Funded by: Apotekerfonden & Danish Ministry for Health

Control group: internal

Baseline BMI, age, physical activity, smoking, education level, cohort, volume, energy intake

Not adjusted for ethnicity

Weighed 7‐day food record

Single dietary assessment used

Moderate

Diabetes Control & Complications Trial (DCCT) & EDIC

Cundiff 2012 (6)

1441 at baseline, 1055 analysed at 14 to 19 years

27% lost or not analysed

Reasons: omitted 137 with HbA1c > 9.5, otherwise losses not described in this publication

Note: also analysed FAO/WHO data from 167 countries, but these appear cross‐sectional

Data not reported

Unclear

Funded by: Data collection by NIH, General Clinical Research Center Program (NCRR), analysis not funded

Control group: internal

Energy, fibre, saturated, mono‐ and poly‐unsaturated fat, alcohol, exercise (probably)

Not adjusted for age, sex, ethnicity or SES

1 week food record (unclear whether recall or diary based)

Multiple dietary assessments (baseline, 2, 5 yrs and completion averaged)

High

EPIC‐PANACEA

Vergnaud 2013 (7)

EPIC

Beulens 2014 (8)

521,448 recruited, 373,803 included in analysis

28% lost or not analysed

Reasons: omitted 23,713 with missing or implausible baseline data, 121,866 with missing follow‐up weight, 2066 with implausible weight changes

Those with lower fat intake tended to be older, more physically active and less likely to smoke

Dissimilar

Funded by: EU and a wide range of charities and government funders

Control group: internal

Adjusted for age, baseline BMI, study centre, weekday, season, total E (from non‐alcohol sources, and from alcohol sources), smoking, education, physical activity

Not adjusted for ethnicity

Quant. dietary questionnaire of 88‐266 items (country‐specific)

Single dietary assessment used

High

Unclear how many were included compared with recruited

unclear% lost or not analysed

Reasons: unclear

Data not reported

Unclear

Funded by: unclear

Control group: internal

Adjustments unclear

Not adjusted for … unclear

Country‐specific FFQs

High

Health Professionals Follow‐Up Study (HPFUS)

Coakley 1998 (9)

USA

36,353 returned 1992 questionnaires, of whom 19,478 were included in this analysis

46% lost or not analysed

Reasons: 9345 had cancer, heart disease, diabetes or stroke, 7530 were missing key information

Data not reported

Unclear

Funded by: NIH and Centres for Disease Control

Control group: internal

Baseline weight, energy, height, activity, TV viewing, high BP, high cholesterol

Not adjusted for ethnicity, socioeconomic status

FFQ

Single dietary assessment used

High

Melbourne Collaborative Cohort Study (MCCS)

MacInnis 2013 (10)

Australia

Of 9066 at baseline, 5879 included in analyses.

35% lost or not analysed

Reasons: 656 died, 1894 declined, 21 did not have waist circumference or weight at follow‐up, and 616 lost ≥ 5 kg weight so excluded

Data not reported

Unclear

Funded by: Cancer Council Victoria, VicHealth, National Health and Medical Research Council

Control group: internal

Weight adjusted for baseline weight, waist for baseline waist circumference. All adjusted for sex, age, physical activity, alcohol, education, smoking, marital status, SES, total energy intake. Not adjusted for ethnicity (all described as "Australian‐born" but > 20% born in Europe)

Self administered 121‐item FFQ developed for study

Single dietary assessment used

High

Memphis

Klesges 1992 (11‐13)

USA

417 were enrolled, 294 were included in weight change analysis, and 230 in the waist circumference change analysis

29% lost or not analysed (weight), 45% (waist)

Reasons: "attrition" for weight change, no explanation of further losses for waist circumference data

Data not reported

Unclear

Funded by: NHLBI and Tennessee Centres of Excellence

Control group: internal

Sex, age, pregnancy status, smoking, alcohol, family risk of obesity, energy intake, sports activity, work activity, leisure activity, change from baseline of energy, fat intake, activity, cigarettes

Not adjusted for socioeconomic status

Willett's FFQ

Single (multiple dietary assessments – but appear to be using baseline data in analysis)

High

NHANES Follow‐up

Kant 1995 (14)

USA

14,407 were enrolled and eligible, 7147 were included in analysis.

50% lost or not analysed

Reasons: no dietary info, unsatisfactory 24‐hour recalls, atypical intake, proxies, mistakes, pregnant or lactating participants, lack of weight data, death

Higher fat as %E associated with younger age, more smoking, higher levels of morbidity

Funded by: unclear

Control group: internal

Baseline age, race, education, BMI, energy intake, smoking, physical activity, duration of follow‐up, alcohol, morbidity, special diet, parity

All adjusted for

24‐hour dietary recall

Single dietary assessment used

High

Nurses' Health Study

Colditz 1990 (15)

Field 2007 (16)

USA

Of 121,700 women enrolled, 38,724 were eligible for this study, 31,940 women included in analyses

17% lost or not analysed

Reasons: non‐respondent or invalid FFQ

Data not reported

Unclear

Funded by: NIH

Control group: internal

Age, BMI, energy intake

Not adjusted for ethnicity, physical activity, socioeconomic status

61‐item FFQ

Single dietary assessment used

High

Of 121,700 women enrolled, 41,518 included in analyses

66% lost or not analysed

Reasons: of 121,700, 41,518 assessed in 1986 and at 8 years, were free of cancer, hypertension and diabetes, and eligible for this study

Greater fat intake associated with greater baseline weight

Unclear

Funded by: Boston Obesity Nutrition Research Center and National Cancer Institute

Control group: internal

Age, baseline BMI, activity, menopausal status, smoking, protein intake, change in protein intake

Not adjusted for ethnicity or SES

136‐item FFQ in 1986

Single dietary assessment

used

High

Pawtucket HHP

Parker 1997 (17)

USA

Of 1081 enrolled, FFQ administered to random sub‐sample of 556, 465 included in analysis

16% lost or not analysed

Reasons: those excluded were those who did not attend both relevant appointments, and were more male, less educated, less active, greater BMI

Data not reported

Unclear

Funded by: NHLBI

Control group: internal

Age, BMI, energy, smoking, activity

Not adjusted for sex, ethnicity or socioeconomic status

Willett's FFQ with categories added for fats, oils, sweets, snacks and dairy products

Single dietary assessment used

High

San Luis Valley Diabetes Study (SLVDS)

Mosca 2004 (18)

USA

Of 1351 enrolled, 782 "included in analysis", unclear how many in prospective analysis

unclear% lost or not analysed

Reasons: unclear how many lost and how many excluded. Of 1351, 1027 had and 782 continued to have normal glucose tolerance tests, 140 altered smoking status or became pregnant and were excluded. 782 completed visit 1, 536 visit 2 and 375 visit 3

Data not reported

Unclear

Funded by: not stated

Control group: internal

Sex, ethnicity, physical activity, baseline BMI, age, smoking status, energy intake

Not adjusted for SES

24‐hour diet recall (bilingual interviewers) with visual aids for food portions

High

SEASONS

Ma 2005 (19)

USA

Of 1257 in original cohort, 641 completed baseline questionnaire and one blood draw, 572 included in analyses

11% lost or not analysed

Reasons: unclear, did not attend further appointments

Data not reported

Unclear

Funded by: NHLBI

Control group: internal

None (but analysed as %E from fat, so energy adjusted for indirectly)

Not adjusted for age, sex, ethnicity, physical activity or socioeconomic status

7‐day dietary recall

Single

(Multiple dietary assessments – but appear to be using baseline data in analysis)

High

Women's Gothenburg

Lissner 1997 (20)

Sweden

Of 1462 in main cohort, 437 randomly selected and asked for dietary information, 361 included in analysis.

17% lost or not analysed Reasons: 64 did not return for weight assessment, 12 had chronic illness so excluded

Higher fat as %E associated with younger age, higher energy intake, more walking and lifting at work, greater likelihood of being a smoker

Funded by: Swedish Medical Research Council

Control group: internal

Baseline body weight, activity, smoking, age, energy

Not adjusted for ethnicity or socioeconomic status

Dietary interview including frequency of 69 food items

Single dietary assessment used

High

Study

Number lost to follow‐up

Baseline similarity, funding, control group

Adjustments*

Method of dietary assessment

Risk of bias**

Adelaide Nutrition Study

Magarey 2001 (1)

Australia

Of 500 recruited to ANS at birth only 130 were seen at age 11, so a further 113 from a separate cohort were added at age 11

˜74% lost (varied for different follow‐ups)

Reason: did not attend

Lost characteristics: not stated

Data not reported

Unclear

Funded by: National Heart Foundation of Australia, Adelaide Children's Hospital Research Foundation, National Health and Medical Research Council of Australia

Control group: internal

Adjusted for energy intake, previous adiposity, adiposity of parent at a specific age

Not adjusted for sex, ethnicity, physical activity or SES (4)

3‐day weighed food record

High

Amsterdam Growth & Health Long. Study (AGAHLS)

Twisk 1998, Koppes 2009 (2;3)

Netherlands

Of 307 13‐year olds recruited 181 were reassessed at age 27

41% lost

Reason: unclear

Lost characteristics: "for the variables of interest no drop‐out effects were observed"

Data not reported

Unclear

Funded by: Dutch Heart Foundation, Dutch Prevention Fund, Dutch Ministry of Wellbeing and Public Health, Dairy Foundation on Nutrition and Health, Netherlands Olympic Committee, Netherlands Sports Fed., no additional funding was stated for the 36‐year old analysis

Control group: internal

Adjusted for physical activity, smoking, alcohol, dietary energy and macronutrient intake. Did not adjust for sex, would have if appropriate.

Not adjusted for ethnicity, parental BMI, or SES (3)

Modified cross‐check dietary history interview relating to previous month

High

Of 698 13‐year olds recruited (those above plus another school with fewer assessments) 350 had complete data at age 36

50% lost

Reason: unclear

Lost characteristics: girls who completed follow‐up had slightly lower body fat %age, and boys who completed had lower tobacco and alcohol use at baseline

Carried out for boys and girls separately, at each age. Skinfold data (not % body fat) additionally adjusted for physical activity

Not adjusted for ethnicity, parental BMI, physical activity or SES (4)

As above

High

Bogaert 2003 (4)

Australia

Of 59 recruited, 41 were re‐assessed at 12 months

31% lost

Reason: unclear

Lost characteristics: unclear

Data not reported

Unclear

Funded by: Australian Rotary Health Found., Financial Markets Found. for Children, National Health & Medical Research Council

Control group: internal

Adjustment not described (or not done) – unclear

Assume not adjusted for age, sex, ethnicity, parental BMI, physical activity or SES (6)

2 food records and 1 24‐hour recall from

High

Carruth & Skinner 2001 (5;6)

USA

Of 72 recruited 53 took part at 70 months

26% lost

Reason: 7 parents declined, 7 not in area, 5 could not be scheduled in timeframe

Lost characteristics: unclear

Data not reported

Unclear

Funded by: Gerber products, Tennessee Agricultural Experiment Station

Control group: internal

Adjusted for BMI (all children white and of same age)

Not adjusted for sex, energy intake, parental BMI, physical activity or SES (5)

3‐day dietary intake interviews by dietitian

High

62 of 72 recruited (98 recruited at 2 mo of age), plus 2 added at 1 year and 6 added at 2 years took part

unclear % lost

Reason: as above?

Lost characteristics: unclear

Adjusted for BMI at 2 years and adiposity rebound age, assessed across ages 2 to 8, all children white and "predominantly middle or upper socioeconomic status"

Factors assessed but found non‐significant so not adjusted for included sex, TV‐watching, parental BMI

All adjusted for (0)

3‐day dietary intake interviews

High

Davison 2001 (7)

197 participants at study entry, 192 re‐assessed 2 years later

3% lost

Reason: unclear

Lost characteristics: none stated

Data not reported

Unclear

Funded by: NIH

Control group: internal

BMI, levels of activity, familial risk of overweight, change in BMI (mother), enjoyment of activity (father), total energy intake (father), and girls' percentage fat intake (girls).

Not adjusted for SES (1)

24‐hour dietary recall

Moderate

ELANCE

Rolland‐Cachera 2013 (8)

France

Unclear how many 10‐month olds, but 222 attended at 10 months and either 2 or 4 years, 73 attended at 20 years, 68 included in analyses.

> 67% lost

Reason: unclear

Lost characteristics: "similar" between those lost to follow‐up and those included

Data not reported

Unclear

Funded by: Institut Benjamin Delessert

Control group: internal

Total energy intake, sex, breast feeding, mother's BMI, father’s occupation

Not adjusted for ethnicity or physical activity (2)

Dietary history (dietitian discussion of diet with parent over past month)

High

European Youth Heart Study

Brixval 2009 (9)

Denmark

384 of 589 baseline children attended follow‐up, 308 in regression model

48% lost

Reason: "due to ethical consideration it was not permitted to contact subjects who decided not to participate at follow‐up"

Lost characteristics: not stated

Data not reported

Unclear

Funded by: not stated

Control group: internal

Age, puberty status, total energy intake, parental income, activity, overweight parents, protein intake, birth weight. Presented by sex

Not adjusted for ethnicity (1)

Interview and questionnaire of children and parents relating to past 24 hours

High

Klesges 1995 (10)

USA

203 children at baseline, 146 at follow‐up

28% lost

Reason: unclear

Lost characteristics: "no significant differences" (P value > 0.15) in BMI, energy intake, fat as %E, physical activity, sex or familial obesity risk between those attending at 2 years and those not attending

Data not reported

Unclear

Funded by: National Heart Lung and Blood Institute

Control group: internal

Age, sex, BMI, physical activity

Not adjusted for ethnicity, SES (2)

Dietary FFQ

High

OMDCC Lee 2012 (11)

Korea

2740+ baseline children (unclear), 1504 followed up

45% lost

Reasons: "analytic sample" – no reasons given

Lost characteristics: unclear

Data not reported

Unclear

Funded by: unclear

Control group: internal

Age, sex, sexual maturation, baseline BMI, exercise, TV time, sleep, parental BMI and education, energy intake, food habits and household income

Not adjusted for ethnicity (1)

24‐hour recall for 2 weekdays and 1 weekend day

High

TAAG

Cohen 2014 (12)

Of 303 randomly selected at baseline, 265 analysed

13% lost

Reasons: 38 did not have complete data

Lost characteristics: no difference in race, age, mother's education

Data not reported

Unclear

Funded by: National Heart Lung and Blood Institute

Control group: internal

Age, ethnicity, physical activity

Not adjusted for energy intake, parental BMI or SES (3)

FFQ

High

Viva la Familia Study Butte 2007 (13)

USA

1030 at baseline, with 879 returning after 1 year

15% lost

Reasons: unclear

Lost characteristics: none stated

Data not reported

Unclear

Funded by: NIH, USDA/ARS

Control group: internal

Adjusted for sex, age, age squared, and Tanner stage and BMI status in Generalised Estimating Equations

Not adjusted for parental BMI, physical activity and SES (3)

24‐hour recall, measured by a registered dietitian

High

Factor assessed

Subgroup

Effect on weight, kg (95% CI)

Number of comparisons

Number of participants

I² for subgroup

Chi² test for subgroup differences

Duration of dietary advice

6 to < 12 months

‐1.7 (‐2.3 to ‐1.1)

10

5305

71%

P value = 0.04

12 to < 24 months

‐2.0 (‐2.5 to ‐1.5)

17

51367

71%

24 to < 60 months

‐1.2 (‐1.7 to ‐0.7)

9

49,286

56%

60+ months

‐0.7 (‐1.7 to 0.3)

4

40,838

58%

Fat intake in the control group assessed during trial (equivalent to baseline fat intake)

> 35%E from fat

‐0.9 (‐1.1 to ‐0.8)

9

45,103

64%

P value < 0.00001

> 30% to 35%E from fat

‐0.8 (‐1.2 to ‐0.5)

9

7123

73%

> 25% to 30%E from fat

‐3.0 (‐3.6 to ‐2.3)

5

2109

1%

Sex

Women only

‐1.4 (‐1.9 to ‐0.9)

15

50,154

72%

P value = 0.20

Men only

‐2.7 (‐4.3 to ‐1.2)

4

1719

76%

Mixed men and women

‐1.1 (‐2.0 to ‐0.2)

5

2492

79%

Year of first publication of the trial

1960s

‐4.1 (‐8.1 to ‐0.1)

1

1450

‐

P value = 0.07

1970s

‐

0

0

‐

1980s

‐0.9 (‐1.8 to ‐0.01)

3

288

0%

1990s

‐1.9 (‐2.6 to ‐1.3)

14

5941

80%

2000s

‐0.9 (‐1.6 to ‐0.3)

6

46,686

77%

2010s

‐

0

0

‐

Difference in %E from fat between intervention and control groups

Up to 5%E from fat

‐0.2 (‐0.9 to 0.6)

5

4567

30%

P value = 0.003

5 to < 10%E from fat

‐2.1 (‐2.9 to ‐1.4)

11

44,356

84%

10 to < 15%E from fat

‐1.3 (‐1.7 to ‐1.0)

4

8311

26%

15+%E from fat

‐3.9 (‐8.8 to 1.0)

3

319

68%

Dietary advice or diet provided

Dietary advice

‐1.6 (‐2.0 to ‐1.1)

22

52,594

78%

P value = 0.04

Diet provided

‐0.7 (‐1.3 to ‐0.1)

1

1741

‐

Dietary fat goals for intervention (these were not necessarily achieved)

30%E from fat

‐1.0 (‐1.7 to ‐0.3)

3

1628

0%

P value = 0.34

25 to < 30%E from fat

‐2.5 (‐4.3 to ‐0.6)

5

509

90%

20 to < 25%E from fat

‐0.9 (‐1.2 to ‐0.6)

5

43,878

31%

15 to < 20%E from fat

‐1.3 (‐2.2 to ‐0.4)

7

7860

58%

Total fat achieved in intervention group

> 30%E from fat

‐0.8 (‐1.3 to ‐0.4)

5

1767

0%

P value = 0.42

≤ 30%E from fat

‐1.1 (‐1.6 to ‐0.6)

13

50,099

76%

BMI at baseline (body mass index, kg/m²)

< 25

‐1.0 (‐1.7 to ‐0.2)

8

1781

56%

P value = 0.17

25 to < 30

‐1.8 (‐2.4 to ‐1.3)

15

51,297

83%

30+

‐1.8 (‐3.5 to ‐0.1)

1

69

‐

Baseline health of participants

Healthy

‐1.0 (‐1.6 to ‐0.4)

3

45,032

87%

P value = 0.12

With risk factors

‐2.2 (‐3.2 to ‐1.2)

12

2166

79%

With disease

‐1.2 (‐1.9 to ‐0.6)

9

6449

44%

Amount of energy reduction in the low fat arm

E intake the same or greater in low fat group

‐0.5 (‐1.5 to 0.5)

4

3352

25%

P value = 0.04

1 to 100 kcal/d less in low fat arm

‐1.5 (‐2.9 to ‐0.1)

4

2398

66%

101 to 200 kcal/d less in low fat arm

‐1.1 (‐2.2 to ‐0.04)

5

43,755

80%

201+ kcal/d less in low fat arm

‐2.2 (‐3.0 to ‐1.5)

8

3954

78%

Trial

Energy intake (SD), kcal

Sugars intake, %E

CHO intake, %E

Protein intake, %E

Alcohol intake, %E

No. of participants

Int.

Cont

Int.

Cont

Int.

Cont

Int.

Cont

Int.

Cont

Int.

Cont

Auckland reduced fat, 1 yr

1887 (672)

2269 (750)

—

—

54.2 (10.5)

45.8 (10.9)

18.4 (3.5)

16.6 (3.9)

3.6 (7.0)

5.7 (7.0)

49

61

BDIT pilot studies, 9 yrs

1460 (376)

1578 (365)

—

—

49.6 (7.5)

46.9 (6.2)

15.5 (2.4)

15.3 (2.6)

2.3 (3.3)

1.7 (2.4)

76

81

BeFIT

(data not reported in control groups)

Bloemberg, Δ to 6 mo

—

—

—

—

4.4 (6.5)

1.2 (6.1)

0.33 (2.9)

0.57 (1.7)

—

—

39

41

BRIDGES, 6 mo

‐34 (79)

+ 22 (79)

—

—

—

—

—

—

—

—

48

46

Canadian DBCP, 2 yrs

1540 (317)

1759 (437)

—

—

60.3 (8.3)

48.8 (8.1)

18.0 (3.2)

16.9 (2.8)

—

—

104

100

De Bont, Δ to 6 mo

‐98 (369)

‐120 (485)

—

—

7.9 (9.5)

‐0.1 (10.9)

2.4 (7.0)

1.7 (5.9)

‐0.2 (1.6)

‐0.4 (2.6)

71

65

DEER (diet alone), Δ to 1 yr

Women:

‐220 (356)

Men:

‐285 (541)

Women: ‐19 (367)

Men:

‐25 (482)

—

—

Women: +5.5 (8.0)

Men: +8.0 (9.3)

Women:

‐0.2 (7.3)

Men: +1.1 (6.6)

—

—

—

—

46, 49

45, 46

DEER (diet and ex), Δ to 1 yr

Women:

‐191 (343) Men:

‐167 (516)

Women:

‐54 (410)

Men: +141 (437)

—

—

Women:

+7.8 (6.2)

Men:

+9.3 (8.3)

Women:

‐0.3 (7.9)

Men:

+1.4 (6.3)

—

—

—

—

43, 48

43, 47

Diet and hormone study, 1 yr

1921 (386)

2063 (610)

—

—

64.3 (9.0)

54.6 (9.2)

14.5 (2.9)

14.1 (3.8)

est: 1 (2)

est: 1 (2)

81

96

Kentucky low fat, 1 yr

1882 (521)

2010 (528)

—

—

53 (8.9)

50 (7.9)

17 (3.4)

18 (4.3)

—

—

47

51

Kuopio, wks 14 to 28

AHA 1791 (382)

Mono 1887 (478)

Low fat 1648 (430)

1982 (406)

—

—

AHA 48 (5)

Mono 47 (6)

Low fat 51 (5)

46 (6)

AHA 17 (2)

Mono 17 (20)

Low fat 19 (3)

16 (2)

—

—

AHA 41

Mono 41

Low fat 40

37

Mastopathy diet, 6 mo

1491 (NR)

1676 (NR)

—

—

56.3 (NR)

48.1 (NR)

17.9 (NR)

15.8 (NR)

4.8 (NR)

4.2 (NR)

10

9

MeDiet, 6 mo

1676 (639)

1654 (498)

18.7 (6.9)

21.9 (9.2)

27.2 (17.0)

25.8 (11.0)

14.9 (4.7)

16.2 (5.1)

5.6 (11.1)

1.6 (2.2)

51?

55?

Moy, 2 yrs

1825 (NR)

2092 (NR)

—

—

—

—

—

—

—

—

117

118

MSFAT, 6 mo

2460 (NR)

2699 (NR)

—

—

47 (NR)

41 (NR)

16 (NR)

14 (NR)

3 (NR)

3 (NR)

117

103

NDHS open 1st

6 mo (for definitions of groups B, C and D see Characteristics of Included Studies)

B: 2154 (432)

C: 2262 (435)

D: 2228 (456)

—

—

B: 48.7 (12.3)

C: 45.3 (12.1)

D: 44.7 (11.7)

B: 18.6 (3.4)

C: 17.6 (3.1)

D: 17.4 (3.1)

B: 3.7 (3.7)

C: 3.6 (4.0)

D: 3.8 (4.0)

B: 339

C: 355

D: 346

NDHS open 2nd

6 mo (for definitions of groups BC, F and G see Characteristics of Included Studies)

BC: 2249 (492)

F: 2196 (427)

G: 2169 (420)

—

—

BC: 45.7 (12.7)

F: 44.1 (11.1)

G: 43.3 (11.4)

BC: 17.3 (3.5)

F: 7.3 (3.0)

G: 17.7 (2.9)

BC: 3.5 (4.2)

F: 4.2 (4.0)

G: 4.0 (4.5)

BC: 491

F: 214

G: 194

Nutrition and breast health, 1 yr

1780 and 1960

1571 and 1687

—

—

—

—

—

—

—

—

23 and 25

24 and 23

Nutrition education study, 6 to 9 mo

1534 (448)

1721 (620)

—

—

43.4 (9.5)

41.5 (8.9)

19.9 (3.7)

18.7 (4.4)

4.5 (7.2)

4.8 (9.3)

224

69

Pilkington, 1 yr

NR

NR

—

—

—

—

—

—

—

—

12

23

Polyp prevention trial, yr 4

1978 (471)

2030 (518)

—

—

58.3 (7.4)

47.1 (7.2)

17.3 (2.5)

16.5 (2.4)

—

—

605

581

Rivellese, 6 mo

NR

NR

14

10

55

48

18

16

—

—

27

17

Simon low fat, 1 yr

1570 (NR)

1594 (NR)

—

—

—

—

—

—

—

—

65

68

Sondergaard, 12 mo

—

—

—

—

52.3 (6.4)

48.5 (8.7)

17.0 (2.9)

16.6 (3.1)

4.5 (5.3)

6.4 (7.4)

62

51

Strychar, 6 mo

NR

NR

—

—

—

—

—

—

—

—

15

15

Swedish breast CA, Δ to 2 yrs

‐215 (P value < 0.01)

‐143 (P value < 0.01)

+4.8 (P value < 0.01)

+1.4 (P value < 0.01)

+11.0 (P value < 0.01)

+2.7 (P value < 0.01)

+1.7 (P value < 0.01)

+0.3 (P value > 0.05)

+0.2 (P value > 0.05)

+0.4 (P value > 0.05)

63

106

Veteran's dermatology, during trial

1995 (564)

2196 (615)

—

—

60.3 (6.3)

44.6 (6.9)

17.7 (2.2)

15.7 (2.4)

3.2 (3.4)

3.2 (3.9)

57?

58?

WHEL, 1 yr

1664 (345)

1635 (384)

—

—

65.3 (8.5)

57.1 (9.3)

—

—

—

—

197

196

WHI, 7.5 yrs

1446 (510)

1564 (595)

—

—

52.7 (9.8)

44.7 (8.5)

—

—

—

—

14246

22083

WHT: feasibility, 2 yrs

1356 (358)

1617 (391)

—

—

59.0 (8.8)

46.9 (8.9)

19.2 (3.9)

16.8 (3.8)

—

—

163

101

WHT: FSMP, Δ to 18 mo

‐488 (NR)

‐255 (NR)

—

—

—

—

—

—

—

—

285

194

WINS, 5 yrs

‐167 (p value < 0.0001 vs cont)

0

—

—

—

—

—

—

—

—

380

648