University of Groningen A life course perspective on diet quality and healthy ageing Vinke, Petra

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A life course perspective on diet quality and healthy ageing

Vinke, Petra

DOI:

10.33612/diss.135998182

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2020

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Vinke, P. (2020). A life course perspective on diet quality and healthy ageing. University of Groningen.

https://doi.org/10.33612/diss.135998182

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Age- and sex-specifi c analyses of diet quality and

4-year weight change in nonobese adults show

stronger associations in young adulthood

Petra C. Vinke

1

_{, Gerjan Navis}

2

_{, Daan Kromhout}

1

_{, Eva Corpeleijn}

1

1 _{University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands} 2_{University of Groningen, University Medical Center Groningen, Department of Nephrology, Groningen, The Netherlands}

The Journal of Nutrition, 2020, 150(3): 560-567

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ABSTRACT

Background: Although the general importance of diet quality in the prevention of

unintentional weight gain is known, it is unknown whether its influence is age or sex dependent.

Objective: The aim of this study was to investigate whether the strength of the

association between diet quality and 4-year weight change was modified by age and sex.

Methods: From the Dutch, population-based Lifelines Cohort, 85,618 non-obese,

adult participants (age 18-93), recruited between 2006 and 2013, were included in the study. At baseline, diet was assessed with a 110-item food frequency questionnaire. The Lifelines Diet Score, based on international evidence for diet-disease relations at the food group level, was calculated to assess diet quality. For analyses, the score was divided in quintiles (Qs). Body weight was objectively measured at baseline and after a median follow-up of 44 months (25th-75th percentile: 35-51 months). In between, body weight was self-reported twice. Linear mixed models were used to investigate the association between diet quality and weight change by sex and in 6 age categories (18-29,30-39,40-49,50-59,60-69,≥70).

Results: Mean 4-year weight change decreased over age categories. Confounder

adjusted linear mixed models showed that the association between diet quality and weight change was modified by sex (P-interaction =0.001). In women, the association was also modified by age (P-interaction =0.001). Poor diet quality was most strongly associated with weight gain in youngest men [Q1 vs. Q5: +0.33 kg/year (95%CI: 0.10-0.56)] and women [+0.22 kg/year (95%CI:0.07-0.37)]. In contrast, in women aged ≥70y, poor diet quality was associated with greater weight loss [-0.44 kg/year (95%CI: -0.84 - -0.05)].

Conclusions: Poor diet quality was related to higher weight gain, especially in young

adults. Oppositely, among women aged ≥70y, poor diet quality was related to higher weight loss. Therefore, a healthful diet is a promising target for undesirable weight changes in both directions.

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5 INTRODUCTION

Gradual but persisting increases in body weight of the worldwide population may jeopardize the achievement of the global obesity target to curb the rise of obesity by

2025 1_{. Weight gain is typically an age-related phenomenon, and the importance of}

lifestyle in its prevention is well-known. However, it is unknown whether it is of equal importance over the life course, and for both sexes.

Diet is a major lifestyle factor relevant for overweight prevention. At the food product level, meta-analyses convincingly showed that sugar-sweetened beverages contributed to weight gain. In adults, one serving per day increase in sugar-sweetened

beverages resulted in an additional 0.22 kg increase of body weight per year 2_{. In}

contrast, foods like fruit, vegetables, yogurt, nuts, and whole grain products were

inversely associated with weight gain in three large prospective cohort studies 3_.

With regard to overall diet quality, prospective cohort studies reported that better adherence to the Mediterranean Diet was associated with lower 2-year weight change

4_{, and lower 3-year obesity incidence in overweight adults}5_{. In addition, a}

meta-analysis of randomized controlled trials showed that Mediterranean Diet adherence positively contributed to intentional weight loss. However, this effect was only statistically significant when the Mediterranean diet was associated with a restriction in energy intake or increase in physical activity, or when trials had a follow-up longer

than 6 months 6_{. However, whether the above results apply to all age-categories, and}

to both men and women is unknown, which is also due to the fact that the majority of described evidence comes from studies including young or middle-aged adults. Weight change can differ between age categories and men and women. A longitudinal study in middle-aged Americans (51-61 years at baseline) showed that the pace at which body mass index (BMI) increased over time was higher among younger men

and women 7_{. Similar findings were reported for younger (18y-24y) compared to older}

(25y-30y) men in the CARDIA Study 8_{, as well as for 20y-29y compared to 40y-49y}

year old men in the longitudinal Tromsø study 9_{. Both studies did not find such age}

differences in women. Although previous studies report a prospective gain in weight, weight gain may not be persistent over the full adult life course. For example, in the American National Health and Nutrition Examination Survey, between 1999 and 2016 body weight was consistently higher in men and women aged 40y-59y than 20y-39y, while after age 60y, body weight was consistently lower than in age 40y-59y

10_{. Regarding sex, a higher general pace of weight gain in men than in women was}

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described in several studies 9,11,12_{, while worldwide obesity prevalence is greater in}

women 1_.

As mentioned above, weight change could be dependent on diet quality. Our previous study investigating diet quality in the Lifelines Cohort illustrated that diet quality was

higher in women, and in older age groups 13_{. Other studies investigating measures of}

diet quality reported similar findings 14,15_{. However, besides differences in diet quality,}

also differences in the magnitude or direction of the association between diet quality and weight change may be involved in age- and sex-specific weight changes. In

the large, contemporary Lifelines Cohort Study 16_{, we integrated age and sex in}

the investigation of diet quality and 4-year weight change, to answer the question whether their association would be uniform or age- and sex-specific.

SUBJECTS AND METHODS

Cohort design and study population

The Lifelines cohort study is a multi-disciplinary prospective population-based cohort study examining in a unique three-generation design, the health and health-related behaviors of 167,729 persons living in the North of the Netherlands. It employs a broad range of investigative procedures in assessing the biomedical, socio-demographic, behavioral, physical and psychological factors which contribute to health and disease of the general population, with a special focus on multi-morbidity and complex genetics. The overall design and rationale of the study have been described in detail

elsewhere 16,17_{. Participants were included in the study between 2006 and 2013, and}

written informed consent was obtained from all participants. The Lifelines study is conducted according to the principles of the Declaration of Helsinki and approved by the Medical Ethics Committee of the University Medical Center Groningen, The Netherlands.

So far, four assessment rounds took place (T1=baseline, median + IQR of time in months to follow-up rounds: T2=13 [12-14], T3=24 [23-27], T4=44 [35-51]). Participants reporting unwanted weight loss at baseline (defined as 6 kilogram (kg)/6 months or 3 kg/1month in the questionnaire) or excessive weight loss between T1 and T4 (>50% of body weight) were excluded from the study. Furthermore, participants reporting pregnancy or chronic diseases which may influence diet or weight (cardiovascular diseases, cancer, diabetes, thyroid disease, and inflammatory bowel diseases) at baseline or during follow-up were excluded. Participants for whom data on diet,

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baseline body weight or covariates were missing or unreliable, were excluded as well. Finally, because this study focuses on the prevention of obesity, and because the process of weight change in obese individuals may involve more genetic, psychiatric and environmental factors than in non-obese individuals, participants who were

already obese at baseline (BMI > 30 kg/m2_{) were excluded as well. Out of 152,662}

adult Lifelines participants, 85,616 met the inclusion criteria and were included in the study (Supplementary Figure 1). Obesity was the most common reason for exclusion (n=24,068).

Data collection

Anthropometrics

At baseline (T1) and T4, height and body weight without shoes and heavy clothing were measured at 12 Lifelines research sites SECA 222 stadiometer and the SECA 761 scale, and rounded to the nearest 0.5 cm and 0.1 kg. Measured weight at T1 and T4 was available for 85,618 and 56,390 participants respectively. At T2 and T3, body weight was self-reported through questionnaires by 71,719 (T2) and 53,390 (T3) participants.

Demographics and lifestyle

Self-administered questionnaires were used to collect baseline data regarding demographics (ethnicity, education) and lifestyle (alcohol, smoking, physical activity). The validated short questionnaire to assess health-enhancing physical activity

(SQUASH) was used to assess physical activity 18_{. Leisure time and Commuting}

Physical activity, including sports, at moderate (4.0-6.4 MET) to vigorous (≥6.5 MET)

intensity (LC_MVPA) was calculated in minutes per week 18_{. Alcohol consumption was}

estimated based on Lifelines’ Food Frequency Questionnaire (FFQ) 19_{. If ethnicity was}

unknown, participants were assumed to be of Dutch nationality, since Dutch is the dominant ethnicity of the cohort (99.0%). Missing data in MVPA were imputed with the

Hot Deck imputation macro for SPSS 20_{, which replaces a missing value with the value}

of a participant who is similar in smoking status, education level and energy intake.

Dietary assessment

At baseline, dietary intake was assessed using a 110-item semi-quantitative FFQ

assessing food intake over the previous month 19_{. Energy intake was estimated from}

the FFQ data by using the 2011 Dutch food composition database 21_{. FFQ data was}

considered unreliable when the ratio between reported energy intake and basal

metabolic rate, calculated with the Schofield equation 22_{, was below 0.50 or above}

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2.75, or when energy intake was below 800 kcal/day (males) or 500 kcal/day (females)

23_.

The Lifelines Diet Score (LLDS) was calculated as a measure of relative diet quality. The development of this food-based diet score has been described in detail

elsewhere 13_{. In short, the LLDS is based on the scientific evidence summarized in the}

29 systematic reviews of international peer-reviewed literature regarding associations of diet with chronic diseases, which the Dutch Health Council performed in the process of the development of the 2015 Dutch Dietary Guidelines. The LLDS ranks the relative intake of nine food groups with proven positive health effects and three

food groups with proven negative health effects 24_{. For each of the food groups,}

quintiles of consumption in grams/1000 kcal are determined and awarded zero to four points (Supplementary Table 1 and 2). For the positive food groups, that is vegetables, fruit, whole grain products, legumes & nuts, fish, oils & soft margarines, unsweetened dairy, coffee, and tea, higher scores are awarded to higher quintiles of consumption. For the negative food groups, that is red & processed meat, butter & hard margarines, and sugar-sweetened beverages, higher scores are awarded to lower quintiles of consumption. The sum of these LLDS components varied from zero to 48. The LLDS scores were then categorized into quintiles, with quintile 1 including 20% of participants with the lowest diet quality and quintile 5 including 20% of participants with the highest diet quality (LLDS range Q1: 0-18, Q2: 19-22, Q3: 23-25, Q4: 26-29, Q5: 30-48). The quintiles for each product group were predefined

in the total Lifelines Cohort 13_.

Data analysis

Self-reported weights are sensitive to underreporting, so self-reported weights at T2

and T3 needed to be adjusted 25_{. At T4, self-reported body weight was available in}

addition to objectively measured body weight. For six age categories separately (18y-29y, 30y-39y, 40y-49y, 50y-59y, 60y-69y, ≥70y), the mean difference between self-reported and measured weight at T4 was calculated and used to adjust self-self-reported weights at T2 and T3. For descriptive purposes, weight change between T1 and T4 was standardized to a 4-year period to account for differences in follow-up time. Subject-specific linear mixed models were fitted to investigate the age- and sex-specific association of diet quality with the change in body weight over time since baseline Models included random intercepts and slopes, allowing each participant to

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have its own starting point and trajectory of change in body weight. The covariance matrix was unstructured. All results were presented crude, as well as multivariable adjusted. The latter were adjusted for potential confounders at baseline including education level (low, moderate, high), smoking status (current, former, never), physical activity (LC_MVPA in min/week), energy intake (kilocalories/day), alcohol

intake (g/day), and BMI at baseline (kg/m2_{). The models also included interactions}

of confounders with time to adjust not only intercepts but also the slopes of weight change.

First, we investigated whether sex modified the association of diet quality and body weight change, by including the three way interaction of sex , LLDS in quintiles and time in years (as well as two-way interactions and variables separately) in a model with body weight as dependent variable. In case of a significant three way interaction in crude and multivariable adjusted models, the following analyses were performed stratified by sex.

In the first model of interest age in 6 categories, LLDS in quintiles, and their interactions with time in years were included in the model as fixed factors, to investigate the differences in weight change among age groups and quintiles of the LLDS. In the second model of interest, the three way interaction of LLDS, age category and time was added to the model, to investigate whether age modified the association of diet quality with body weight change in men or women. Analyses were then also stratified by age category to illustrate the age-specific associations of diet quality and weight change.

In a sensitivity analysis in 51.816 participants we investigated whether the association between diet quality and weight change was explained by changes in muscle mass, by including change in 24-hour urinary creatinine excretion as a proxy for muscle

mass 26_.

In all models, the most weight stable age category was used as the reference (age 50-59 years). For the LLDS, quintile 5 representing the highest diet quality was set as the reference. Data analysis was performed in IBM SPSS 23. (SPSS, Chicago Illinois, USA). P-values below 0.05 were considered to represent significant results.

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110 Ta b le 1 . B as e lin e c h ar ac te ri st ic s o f 8 5 ,6 16 a d u lt L ife lin e s p ar ti ci p an ts i n cl u d e d i n t h is s tu d y, s tr at ifi e d b y a g e c at e g o ry a n d s ex ( fe m a le s o n n ex t p a g e). To ta l M a le s n =8 5 ,61 6 18 y-2 9 y n =5 ,8 6 2 3 0 y-3 9 y n =8 ,3 20 40 y-4 9 y n =1 2, 9 0 8 50 y-59 y n =6 ,4 35 6 0 y-6 9 y n =3 ,5 72 ≥70 y n =83 3 D emo g rap h ic s A g e (ye ar s) 1 43 .4 ± 1 2. 5 24 .8 ± 3 .4 34 .7 ± 2 .9 4 4 .7 ± 2 .9 53 .6 ± 3 .0 63 .7 ± 2 .6 73 .7 ± 3 .7 W h ite , E as t/ W e st E u ro pe an E thni ci ty 2 9 9 .0 9 8 .9 9 8 .9 9 9 .1 9 8 .9 9 9 .2 9 9 .4 Ed u ca ti o n 2 Lo w 26 .0 15 .0 17. 7 26 .7 31 .7 41 .2 45 .3 M od er a te 41 .0 53 .2 41 .5 39 .5 34 .3 25 .6 23 .3 Hi gh 33 .0 31 .8 40. 8 33 .8 34 .0 33 .2 31 .5 An thr op om e tr y W e ig h t a t b as e lin e ( kg ) 1 76 .3 ± 1 2. 1 8 0 .4 ± 1 0 .5 8 5 .0 ± 1 0 .3 8 6 .5 ± 9 .8 8 5 .3 ± 9 .5 8 3 .0 ± 9 .0 8 0 .8 ± 8 .0 B o d y M as s I n d e x ( kg /m 2) 1 24 .6 ± 2 .7 23 .7 ± 2 .7 25 .0 ± 2 .5 25 .6 ± 2 .4 25 .7 ± 2 .3 25 .8 ± 2 .2 25 .7 ± 2 .2 Li fe st yle L ife lin e s D ie t S co re 1 23 .9 ± 6 .1 19 .7 ± 5 .4 21 .7 ± 5 .4 22 .5 ± 5 .4 24 .0 ± 5 .5 25 .1 ± 5 .6 25 .3 ± 5 .4 E n e rg y I n ta ke ( kc al /d ay ) 1 21 0 2 ± 6 25 25 42 ± 7 14 25 0 0 ± 6 45 24 6 2 ± 6 49 23 29 ± 6 0 5 21 52 ± 5 41 20 8 5 ± 49 6 A lc o h o l U ser per cen ta g e 2 85 .6 9 4 .1 9 2.3 91 .6 92 .8 91 .5 8 7. 3 C on sum p tion a m on g c on sume rs (g / d a y) 3 6. 4 [2. 6 –1 2. 4] 9 .8 [4 .7 –1 7. 1] 7. 1 [3 .4 –1 5 .1 ] 7. 1 [3 .2 –1 5 .8 ] 9 .5 [4 .1 –1 7.1 ] 9 .8 [4 .4 –1 7.4 ] 8. 2 [3 .1– 16 .6 ] S m o ki n g 2 C u rr en t Sm o ker 21 .1 31 .5 26 .9 22 .5 18 .7 13 .7 8 .5 For me r S m ok er 30. 6 9 .6 23 .8 27. 7 42. 4 56 .2 69 .4 N ev er Sm o ker 48 .3 58 .9 49 .3 49 .8 38.8 30 .1 22 .1 LC -M P VA 4 (m in /w e e k) 3 20 5 [7 5–3 8 5] 270 [10 0 –4 6 0 ] 18 0 [6 0 – 3 6 0 ] 18 0 [6 0 – 3 6 0 ] 210 [6 0 -40 5] 240 [1 00 -4 80 ] 300 [1 20 -5 70 ] 135558_Petra_Vinke_BNW-def.indd 110 135558_Petra_Vinke_BNW-def.indd 110 3-9-2020 14:33:143-9-2020 14:33:14

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Ta b le 1 . C o nt in u ed Fe m a le s 18 y-2 9 y n =7 ,3 57 3 0 y-3 9 y n =9 ,3 52 40 y-4 9 y n =1 7, 15 3 50 y-59 y n =8 ,6 56 6 0 y-6 9 y n =4 ,3 31 ≥70 y n =8 37 D emo g rap h ic s A g e (ye ar s) 1 23 .5 ± 3 .5 35 .2 ± 2 .8 4 4 .7 ± 2 .9 53 .6 ± 3 .0 63 .6 ± 2 .7 73 .5 ± 3 .4 W h ite , E as t/ W e st E ur op e an E th n ic it y 2 9 9 .0 9 8. 4 9 8.8 9 9 .3 9 9 .5 9 9 .3 Ed u ca ti o n 2 Lo w 12 .7 14 .1 23 .3 37. 0 59 .2 69 .5 M od er a te 53 .4 43 .8 46 .2 36.5 20 .2 14 .9 Hi gh 33 .9 42 .1 30. 5 26.5 20 .5 15 .5 An thr op om e tr y W e ig h t a t b as e lin e ( kg ) 1 6 7. 3 ± 9 .3 6 9 .9 ± 9 .4 70 .5 ± 9 .0 70 .0 ± 8 .7 6 9 .5 ± 8 .3 6 8 .4 ± 8 .3 B o d y M as s I n d e x ( kg /m 2) 1 23 .0 ± 2 .8 23 .9 ± 2 .8 24 .3 ± 2 .7 24 .6 ± 2 .7 25 .1 ± 2 .6 25 .4 ± 2 .7 Li fe st yle L ife lin e s D ie t S co re 1 21 .9 ± 6 .0 24 .0 ± 5 .7 25 .0 ± 5 .8 27 .2 ± 5 .8 28 .2 ± 5 .6 27 .9 ± 5 .6 E n e rg y I n ta ke ( kc al /d ay ) 1 18 12 ± 4 74 19 0 4 ± 4 8 3 18 8 7 ± 4 75 18 12 ± 4 48 17 40 ± 4 19 17 0 1 ± 3 8 6 A lc o h o l U ser per cen ta g e 2 8 6 .1 76 .7 78 .7 83 .3 8 0 .1 71 .2 C on sum p tion a m on g c on sume rs (g /d a y) 3 3 .7 [1 .8 – 6 .9 ] 3 .2 [1 .5 – 6. 9 ] 4 .9 [1 .7 – 9 .3 ] 6. 4 [2. 5– 12 .1 ] 6 .6 [2. 6 –1 2. 2] 6 .1 [1 .8 –10 .9 ] S m o ki n g 2 C u rr en t Sm o ker 25 .9 21 .3 19 .7 18 .4 9 .9 3 .6 For me r S m ok er 9 .1 24 .4 30. 2 47. 6 49 .3 40 .3 N ev er Sm o ker 6 5 .0 5 4 .3 50 .1 34 .0 40. 8 56 .2 LC -M P VA 4 (m in /w e e k) 3 23 0 [1 00 -4 00 ] 18 0 [7 2 – 3 30 ] 18 0 [7 1 – 3 6 0 ] 210 [9 0 – 3 9 0 ] 270 [1 20 -4 8 0 ] 240 [1 20 - 4 8 0 ] 1 M ea n ± S D f o r n o rm a lly d is tr ib u te d d a ta 2 P er ce nt a g e ( % ) 3 Me d ia n [ 25 th – 75 th p er ce nt ile ] f o r s ke w ed d a ta 4 L ei sur e t ime a n d C omm u tin g M o d er a te a n d V ig o ro u s P hy si ca l A ct iv ity 135558_Petra_Vinke_BNW-def.indd 111 135558_Petra_Vinke_BNW-def.indd 111 3-9-2020 14:33:143-9-2020 14:33:14

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RESULTS

Study population

Baseline characteristics of the study population (55.7% female, age 18-93) showed differences between men and women, and between age categories (Table 1). Education level was lower in older age groups, especially in women. Mean body weight was 84.5 ± 10.1 kg for men and 69.7 ± 9.1 kg for women. Mean LLDS in the highest compared to the youngest age group was 5.6 points (approximately 1 SD) higher in men and 6 points higher in women. For all age categories, mean LLDS was approximately 2 to 3 points higher in women than in men. No relevant differences in baseline characteristics were observed between participants with complete and incomplete follow-up (Supplementary Table 3).

Observed change in measured body weight

For illustration, Figure 1 shows baseline body weight in the six age categories, combined with the change in measured body weight between T1 and T4. Body weight values showed a curve over the life course and peaked in age category 40-49 years. From the youngest to highest age group, mean 4-year change in measured body weight ranged from 1.76 kg to

-1.12 kg in men and from 1.19 kg to -0.68 kg in women (Table 2). Both absolute and relative to mean baseline body weight, weight gain in young adults (18y-29y) and weight loss in older adults (≥70y) is more extreme in men. However, over the life course, weight gain was more persisting in women, illustrated by a comparable weight gain in all age categories until age 50y. With regard to diet quality, mean weight change in the lowest quintile of the LLDS was 1.29 kg and 0.75 kg greater than in the highest quintile of the LLDS, in men and women respectively (Table 2).

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Figure 1. Illustration of mean body weight and 4-year weight change in A) men and B) women over

the life course. Body weight in kg, at baseline (T1) and 4th assessment (T4). Unadjusted data from 24,926 male and 31,475 female participants who had both T1 and T4 measurements. Interval between T1 and T4 standardized to 4 year.

Table 2. Observed 4-year weight change among age categories and levels of diet quality, in both

men and women.

Weight change1

Males2 _Females2

Absolute (kg)

(mean ± SD) Relative to body weight (%) Absolute (kg)(mean ± SD) Relative to body weight (%)

Age category Age 18-29 1.76 ± 6.15 2.19 1.19 ± 6.17 1.77 Age 30-39 0.19 ± 4.99 0.23 0.78 ± 5.61 1.11 Age 40-49 0.17 ± 4.78 0.19 0.83 ± 5.12 1.17 Age 50-59 -0.30 ± 4.35 -0.36 0.07 ± 4.73 0.10 Age 60-69 -0.84 ± 4.12 -1.02 -0.37 ± 4.06 -0.54 Age ≥70 -1.12 ± 3.76 -1.38 -0.68 ± 3.49 -0.99 Diet quality (LLDS quintile)3 Q1 (1-18) 0.79 ± 5.36 0.95 1.02 ± 5.63 1.47 Q2 (19-22) 0.25 ± 4.94 0.29 0.77 ± 5.29 1.10 Q3 (23-25) 0.12 ± 4.88 0.14 0.66 ± 5.13 0.94 Q4 (26-29) -0.31 ± 4.56 -0.37 0.37 ± 5.00 0.53 Q5 (30-46) -0.50 ± 4.41 -0.60 0.27 ± 5.04 0.39

1_{Weight change between T1 and T4, interval standardized to 4 year.}

2_{Table based on 24,925 male and 31,475 female participants with measured body weight at T1 and T4.}

3_{Range of LLDS in each quintile indicated in brackets.}

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Sex-specific associations of age and diet quality with weight

change

Sex significantly modified the association of diet quality and body weight change

(P-crude= 0.005, P-adjusted = 0.001) and further analyses were therefore stratified

by sex. In both men and women, age and diet quality were independently associated with weight change in the crude and adjusted model (p <0.001 for fixed effects) (Table

3). Multivariable adjusted estimates for age categories illustrated that weight gain

was approximately 0.15 kg/year higher in women in the three youngest age groups (18y-29y, 30y-39y, 40y-49y) than in the reference category, age 50y-59y. For men, multivariable adjusted yearly weight change in comparison to the reference group, 50-59 years, was 0.32 (95%CI: 0.27 – 0.37) kg higher in

18-29 year olds, which was approximately three times higher than estimates for men aged 30y-39y (0.09 kg/year (95%CI: 0.05 – 0.13)) and 40y-49y (0.11 kg/year (95%CI: 0.07 – 0.15)). Regarding diet quality, the multivariable adjusted model estimated that yearly weight change, regardless of age category, in quintile 1 compared to reference quintile 5, was 0.15 (95%CI: 0.10 – 0.20) kg higher in men, and 0.12 (95%CI: 0.07 – 0.16) kg in women.

Table 3. Associations of age accounting for differences in diet quality, and associations of diet quality

accounting for differences in age, with yearly weight change1_.

Males

(n=37,390) (n=47,686)Females

Crude Adjusted2 _Crude _Adjusted2

Age category

Age 18-29 _{(0.411 ; 0.508)}0.459 _{(0.270 ; 0.371)}0.320 _{(0.148 ; 0.240)}0.194 _{(0.097 ; 0.194)}0.145 Age 30-39 _{(0.073 ; 0.159)}0.116 _{(0.045 ; 0.132)}0.088 _{(0.101 ; 0.183)}0.142 _{(0.095 ; 0.180)}0.138 Age 40-49 _{(0.064 ; 0.141)}0.102 _{(0.069 ; 0.147)}0.108 _{(0.121 ; 0.191)}0.156 _{(0.118 ; 0.190)}0.154

Age 50-59 ref ref ref ref

Age 60-69 _{(-0.171 ; -0.068)}-0.120 _{(-0.172 ; -0.069)}-0.121 _{(-0.148 ; -0.051)}-0.099 _{(-0.147 ; -0.048)}-0.097 Age ≥70 _{(-0.274 ; -0.084)}-0.179 _{(-0.282 ; -0.092)}-0.187 _{(-0.285 ; -0.088)}-0.186 _{(-0.293 ; -0.094)}-0.193

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Table 3. Continued

Males

(n=37,390) (n=47,686)Females

Crude Adjusted2 _Crude _Adjusted2

Diet quality (LLDS quintile)3 Q1 (1-18) _{(0.127 ; 0.224)}0.175 _{(0.104 ; 0.204)}0.154 _{(0.076 ; 0.164)}0.120 _{(0.071 ; 0.164)}0.118 Q2 (19-22) _{(0.054 ; 0.148)}0.101 _{(0.058 ; 0.154)}0.106 _{(0.032 ; 0.109)}0.070 _{(0.034 ; 0.114)}0.074 Q3 (23-25) _{(0.035 ; 0.133)}0.084 _{(0.043 ; 0.142)}0.092 _{(0.011 ; 0.088)}0.050 _{(0.020 ; 0.099)}0.060 Q4 (26-29) _{(-0.034 ; 0.065)}0.016 _{(-0.026 ; 0.072)}0.023 _{(-0.031 ; 0.041)}0.005 _{(-0.024 ; 0.049)}0.013

Q5 (30-46) ref ref ref ref

1_{Estimates (β) and 95% Confidence Intervals for weight change per year in kg from crude and adjusted linear}

mixed models.

2_{Adjusted for baseline education level, smoking status, energy intake, alcohol intake, BMI , LC-MVPA and their}

interactions with time, so that both intercept and slope were adjusted for confounders.

3_{Range of LLDS in each quintile indicated in brackets.}

Age-specific associations of diet quality and weight change

The association between diet quality and weight change was significantly modified by age category in women (p=0.001 for both crude and adjusted analyses), but not in men (p=0.111 and p=.139 for crude and adjusted analyses), Figure 2 illustrates the estimated association of diet quality and annual weight change in analyses stratified by age category (Supplementary Table 4 and 5). In both men and women, the association of poor diet quality (LLDS Q1) with weight gain was strongest in the youngest age group (Q1 vs. Q5 = 0.33 kg/year (95%CI: 0.10-0.56) in men and 0.22 kg/year (95%CI:0.07-0.37) in women). In addition, in older women (age ≥70y), poor diet quality was associated with higher weight loss (Q1 vs. Q5 = -0.44 kg/year (95%CI: -0.84 - -0.05)). The sensitivity analysis including change in 24-hour creatinine excretion attenuated the estimates, but showed a similar pattern (Supplementary Table 6 and 7/ Supplementary Figure

2). The number of participants and body weight observations per quintile of the LLDS,

across strata of sex and age can be found in Supplementary Table 8 and 9.

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Figure 2. Diet quality in association with estimated annual weight change by age category, in A) 37,930

men and B) 47,686 women. Reference quintile 5 (right) of the Lifelines Diet Score represents highest diet quality. Estimates derived from age-stratified linear mixed models adjusted for baseline educa-tion level, smoking status, energy intake, alcohol intake, BMI , leisure time and commuting MVPA and their interactions with time, so that both intercept and slope were adjusted for confounders. LLDS range per quintile: Q1: 1-18, Q2: 19-22, Q3: 23-25, Q4: 26-29, Q5: 30-46.

DISCUSSION

This large prospective study in non-obese adults of the contemporary Dutch Lifelines cohort showed that the association of diet quality with weight change differed over the life course and between men and women. The association between poor diet quality and weight gain was strongest in young adults (age 18y-29y). In elderly women, on the other hand, the association of diet quality and weight change was reversed (≥70y), meaning that lower diet quality was associated with greater weight loss. This illustrates the relevance of a healthful diet in the prevention of undesirable weight changes over the life course.

The relation of diet as a determinant of weight gain goes beyond diet quantity, illustrated by various observational cohort studies reporting inverse associations of measures of diet quality with overweight- and obesity-related outcomes, also after

adjustment for energy intake 27_{. The overall, adjusted estimates for the association}

of diet quality with weight change found in our study (0.15 and 0.12 kg/year in men and women, for Q1 vs Q5) were comparable to other prospective studies with body weight as outcome measure. For example, in the Framingham Offspring study (mean age 51.7y), the difference in weight change between the lowest versus highest score of the five-point Diet Quality Index was approximately 0.14 and 0.17 kg/year in men

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and women respectively 28_{. In men of an Australian population-based cohort (aged}

25y-75y), yearly change in BMI in the lowest vs. the highest quartile of diet quality

was 0.06 kg/m2_{higher, which equals to 0.17 kg for an individual with a height of 1.70}

meter and a BMI of 25. No association was found for women 29_.

Our results illustrated that these general associations, and even the absence of an association in women, may not be true for all age categories. By illustrating the age-specificity of the association of diet quality and changes in weight, we showed that these general associations may represent a combination of strong inverse associations in young, and weaker or reversed associations in older participants. Though, it must be noted that the evidence described here comes from prospective cohort studies, which do not provide information on causality. A meta-analysis of randomized controlled trials on the effect of the Mediterranean diet on weight loss did not find a significant effect on weight loss when the diet was not combined with a

restriction in energy intake 6_{. However, many RCT’s only have short-term follow-up, so}

to be able to further investigate the causal effect of diet quality beyond diet quantity, on change in body weight, long-term RCT’s are needed.

In both men and women, age-specific associations between diet quality and weight change showed a gradual decrease in strength over the age categories. The strongest inverse association was found in the youngest age group, where men with a poor diet quality (Q1) gained on average 1.3 kg more over 4 years than men adhering to a high quality diet. For women, this was almost 1 kg. The age-trend was most pronounced in women, where age was a significant modifier of the association of diet quality with weight change. Specifically in the youngest age category, baseline body weight was still relatively low. This emphasizes the potential of a healthful diet in early adulthood, when overweight may still be preventable.

In contrast to the general finding that a poor diet quality was related to higher weight gain, elderly women (age ≥70y) adhering to a poor quality diet lost approximately 1.8 kg more weight in 4 years than women with a high quality diet. The question was raised whether this was a favorable loss of fat mass, or whether this weight loss was accompanied by a potentially unfavorable loss of muscle mass. In a sensitivity analysis, we estimated that 0.5 kg of this weight change was explained by loss of muscle mass. However, it must be noted that change in 24 hour creatinine excretion was used as a proxy for change in muscle mass, which may not have captured

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change in muscle mass accurately. In elderly, loss of body mass and muscle mass is

associated with higher mortality risk 30–32_{. That we could not replicate these findings in}

men may be related to results of a prior study, in which malnutrition was a risk factor

for sarcopenia in elderly women, but not in men33_{. Together, our results showed that,}

by limiting weight gain in young adults and limiting weight loss in elderly women, a high quality diet may contribute to the prevention of undesirable weight changes in both directions.

Strengths of our study include the large, contemporary cohort, which enabled stratification by age and sex while maintaining sufficient power. In addition, the novel food-based LLDS to express diet quality is in line with current scientific evidence on diet-disease relationships. Although information regarding many potential confounding factors was available, residual confounding remains possible. For example, we were not able to adjust for menopausal status in women, which could have introduced bias, primarily in analyses in women aged 40y-60y. Furthermore, although linear mixed models allow the inclusion of participants with incomplete follow-up, it is possible that survival bias exists. Death is often preceded by a loss of weight, and the absence of these data points could have contributed to an underestimation of the magnitude of weight loss in the oldest age categories. We hypothesize that this would be primarily the case in males, due to a higher number of deaths in the male Lifelines population (in July 2019, 761 males and 530 females of the cohort had passed away). Another limitation is that dietary information was only

available at baseline. However, based on Smith et al.34_{, who compared the association}

of baseline diet and change in diet with weight gain, the most likely expectation is that the lack of information on dietary change underestimated the association between diet quality and weight change in this study. Lastly, since we excluded obese participants because of the focus on prevention of obesity rather than treatment. The exclusion of this subgroup of the population may have introduced bias in our estimates of weight change, since it would be expected that heavier individuals would contribute to a decrease in mean weight change due to regression to the mean. Our results should therefore be interpreted with caution and are only applicable to generally healthy, non-obese individuals.

In summary, this large study in non-obese adult participants of the Lifelines cohort showed greater weight gain among younger age groups and among groups with lower diet quality. More importantly, the association between a poor diet quality and

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weight gain was strongest in young adults (aged 18y-29y), both in men and women. Combined with the fact that prevalence of overweight is still relatively low in young Dutch adults, we conclude that young adulthood may be a window of opportunity for weight gain prevention through diet quality improvement. On the other hand, adhering to a healthy diet was related to lower weight loss in elderly women (aged ≥70y). Thus, the same dietary guidelines for the prevention of chronic diseases may contribute both to prevention of weight gain in young, and weight loss in elderly women. In future research, study populations should be extended towards childhood to further focus on optimal windows of opportunity for weight gain prevention.

Acknowledgements

The Lifelines Biobank initiative has been made possible by a subsidy from the Dutch Ministry of Health,Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG the Netherlands), University of Groningen, and the Northern Provinces of the Netherlands. Furthermore, we thank Dr. Sacha La Bastide-van Gemert for her input regarding the statistical analyses. The authors’ responsibilities were as follows—PCV, GN, DK, and EC: designed the study; PCV and EC: conducted the research and performed the statistical analysis of the data; PCV, GN, DK, and EC: wrote the paper; EC: had the primary responsibility for the final content; and all authors: read and approved the final manuscript.

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REFERENCES

1. Di Cesare M, Bentham J, Stevens GA, et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet 2016; 387: 1377–1396.

2. Malik VS, Pan A, Willett WC, Hu FB. Sugar-sweetened beverages and weight gain in children and adults : a systematic review and meta-analysis. Am J Clin Nutr 2013; 98: 1084–102.

3. Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in Diet and Lifestyle and Long- Term Weight Gain in Women and Men. N Engl J Med 2011; 364: 2392–2404.

4. Sánchez-Villegas A, Bes-Rastrollo M, Martínez-González MA, Serra-Majem L. Adherence to a Mediterranean dietary pattern and weight gain in a follow-up study: the SUN cohort. Int J Obes 2006; 30: 350–358.

5. Mendez M a, Popkin BM, Jakszyn P, et al. Adherence to a Mediterranean diet is associated with reduced 3-year incidence of obesity. J Nutr 2006; 136: 2934–2938.

6. Esposito K, Kastorini C-M, Panagiotakos DB, Giugliano D. Mediterranean Diet and Weight Loss: Meta-Analysis of Randomized Controlled Trials. Metab Syndr Relat Disord 2011; 9: 1–12.

7. Botoseneanu A, Liang J. Social stratification of body weight trajectory in middle-age and older Americans: Results from a 14-year longitudinal study. J Aging Health 2011; 23: 454–480. 8. Burke G, Bild DE, Hilner JE, et al. Differences in weight gain in relation to race, gender, age, and

education in young adults: The CARDIA study. Ethnicity and Health 1996; 1: 327–335.

9. Jacobsen BK, Njolstad I, Thune I, et al. Increase in weight in all birth cohorts in a general population: The Tromso Study, 1974-1994. Arch Intern Med 2001; 161: 466–472.

10. Fryar CD, Kruszon-Moran D, Gu Q, Ogden CL. Mean body weight, height, waist circumference, and body mass index among adults: United States, 1999-2000 through 2015-2016. Natl Health

Stat Report 2018; 2018: 1–16.

11. Chiriboga DE, Ma Y, Li W, et al. Gender differences in predictors of body weight and body weight change in healthy adults. Obesity 2008; 16: 137–145.

12. Schulz M, Nöthlings U, Hoffmann K, Bergmann MM, Boeing H. Identification of a Food Pattern Characterized by High-Fiber and Low-Fat Food Choices Associated with Low Prospective Weight Change in the EPIC-Potsdam Cohort 1. J Nutr 2005; 135: 1183–1189.

13. Vinke PC, Corpeleijn E, Dekker LH, et al. Development of the Lifelines Diet Score (LLDS) and its application in 129,369 Lifelines participants. Eur J Clin Nutr 2018; 72: 1111–1119.

14. Wang DD, Leung CW, Li Y, et al. Trends in dietary quality among adults in the United States, 1999 through 2010. JAMA Intern Med 2014; 174: 1587–1595.

15. Guenther PM, Kirkpatrick SI, Reedy J, et al. The Healthy Eating Index-2010 Is a Valid and Reliable Measure of Diet Quality According to the 2010 Dietary Guidelines for Americans. J Nutr 2014; 144: 399–407.

16. Scholtens S, Smidt N, Swertz MA, et al. Cohort Profile: LifeLines, a three-generation cohort study and biobank. Int J Epidemiol 2015; 44: 1172–1180.

17. Stolk RP, Rosmalen JGM, Postma DS, et al. Universal risk factors for multifactorial diseases: LifeLines: A three-generation population-based study. Eur J Epidemiol 2008; 23: 67–74.

(20)

121

5

18. Wendel-Vos GCW, Schuit AJ, Saris WHM, Kromhout D. Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol 2003; 56: 1163–1169.

19. Siebelink E, Geelen A, de Vries JHM. Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults. Br J Nutr 2011; 106: 274–81.

20. Myers TA. Goodbye, Listwise Deletion: Presenting Hot Deck Imputation as an Easy and Effective Tool for Handling Missing data. Commun Methods Meas 2011; 5: 297–310.

21. NEVO-tabel Dutch Food Composition Table 2011. RIVM/Dutch Nutrition Centre, 2011.

22. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum

Nutr Clin Nutr 1985; 39 Suppl 1: 5–41.

23. Willett W. Issues in Analysis and Presentation of Dietary Data. In: Nutritional Epidemiology. Oxford University Press. 2012.

24. Kromhout D, Spaaij CJK, de Goede J, Weggemans RM. The 2015 Dutch food-based dietary guidelines. Eur J Clin Nutr 2016; 70: 869–78.

25. Visscher TLS, Viet AL, Kroesbergen HT, Seidell JC. Underreporting of BMI in adults and its effect on obesity prevalence estimations in the period 1998 to 2001. Obesity 2006; 14: 2054–2063. 26. Alexandrov N V., Eelderink C, Singh-Povel CM, et al. Dietary protein sources and muscle mass

over the life course: The lifelines cohort study. Nutrients 2018; 10: 1–17.

27. Asghari G, Mirmiran P, Yuzbashian E, Azizi F. A systematic review of diet quality indices in relation to obesity. Br J Nutr 2017; 117: 1055–1065.

28. Quatromoni PA, Pencina M, Cobain MR, Jacques PF, D’Agostino RB. Dietary Quality Predicts Adult Weight Gain: Findings from the Framingham Offspring Study. Obesity 2006; 14: 1383–1391. 29. Arabshahi S, van der Pols JC, Williams GM, Marks GC, Lahmann PH. Diet quality and change in

anthropometric measures: 15-year longitudinal study in Australian adults. Br J Nutr 2012; 107: 1376–1385.

30. Nguyen ND, Center JR, Eisman JA, Nguyen T V. Bone loss, weight loss, and weight fluctuation predict mortality risk in elderly men and women. J Bone Miner Res 2007; 22: 1147–1154.

31. Harrington M, Gibson S, Cottrell RC. A review and meta-analysis of the effect of weight loss on all-cause mortality risk. Nutr Res Rev 2009; 22: 93.

32. Marzetti E, Calvani R, Tosato M, et al. Sarcopenia: an overview. Aging Clin Exp Res 2017; 29: 11–17. 33. Tay L, Ding YY, Leung BP, et al. Sex-specific differences in risk factors for sarcopenia amongst

community-dwelling older adults. Age 2015; 37: 1–12.

34. Smith JD, Hou T, Hu FB, et al. A Comparison of Different Methods for Evaluating Diet, Physical Activity, and Long-Term Weight Gain in 3 Prospective Cohort Studies. J Nutr 2015; 145: 2527–2534.

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SUPPLEMENTARY MATERIALS

Supplementary Figure 1. Flowchart of exclusion study participants.

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Supplementary Table 1. Cut-points for quintiles of positive and negative food groups in total adult

Lifelines Cohort (n =129.363). Cut-points are in grams/1000 kcal. Negative groups are scored inversely, so above cut-point Q1-Q2 is included in Q1, below cut-point between Q4-Q5 is included in Q5.

Food group Q1 to Q2 Q2 to Q3 Q3 to Q4 Q4 to Q5

Positive groups

Vegetables 27.03 41.07 55.17 75.52

Fruit 18.43 41.78 73.87 118.79

Whole grain products 33.61 47.87 60.09 74.32

Legumes & Nuts 3.11 6.48 10.63 16.50

Fish 1.28 3.89 6.21 9.35

Oils & Soft margarines 1.74 5.02 10.28 15.91

Unsweetened dairy 21.65 55.67 93.51 149.58

Coffee 79.44 169.64 236.37 323.12

Tea 10.86 58.57 124.51 225.51

Negative groups

Red & Processed meat 45.21 36.17 29.05 20.69

Butter & Hard margarines 19.70 13.18 8.10 3.19

Sugar-sweetened beverages 117.71 64.20 30.00 8.78

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Supplementary Table 2. Characteristics of Lifelines Participants in different quintiles of the Lifelines

Diet Score.

Lifelines Diet Score quintile

Q1 Q2 Q3 Q4 Q5

Lifelines Diet Score range 0-18 19-22 23-25 26-29 30-46

Demographics

Female1 _41.9 _48.4 _54.9 _61.6 _73.1

Age (years)2 _{37.8 ± 11.9} _{41.4 ± 12} _{43.8 ± 11.9} _{45.8 ± 12.1} _{48.5 ± 11.8} White, East/West European

Ethnicity1 99.0 98.9 99.0 99.0 98.8 Education1 Low 29.9 26.8 25.3 24.6 23.5 Moderate 47.6 43.6 41.2 38.2 33.9 High 22.5 29.6 33.6 37.2 42.6 Lifestyle Body weight (kg)2 Male 83.9 ± 10.6 84.9 ± 10.1 85.2 ± 10 84.8 ± 9.6 83.6 ± 9.8 Female 69 ± 9.5 69.8 ± 9.2 70.1 ± 9 70 ± 9 69.4 ± 8.9

Body Mass Index (kg/m2₎2

Male 25.2 ± 2.7 25.4 ± 2.6 25.5 ± 2.6 25.3 ± 2.5 25.1 ± 2.6

Female 24.2 ± 3.1 24.4 ± 3.1 24.5 ± 3 24.6 ± 3.1 24.5 ± 3.1

Energy Intake (kcal/day)2

Male 2621 ± 712 2494 ± 642 2387 ± 616 2268 ± 562 2117 ± 527 Female 2033 ± 520 1945 ± 475 1880 ± 453 1806 ± 441 1675 ± 399 Alcohol User percentage1 _85.1 _86.0 _85.8 _85.6 _85.3 Consumption among consumers (g/day)3 6.8 [2.7 - 16.1] [2.6 - 12.9]6.6 [2.5 - 12.3]6.4 [2.5 - 11.7]6.3 [2.5 - 10.2]6.2 Smoking1 Current Smoker 31.8 24.4 20.5 16.4 11.9 Former Smoker 21.6 26.8 31.0 34.2 40.0 Never Smoker 46.7 48.8 48.5 49.4 48.1 Leisure time/Commuting Moderate + Vigorous Physical Activity (min/week)3

150

[45 - 345] [60 - 360]180 [75 - 366]195 [90 - 390]220 [120 - 450]255

1_%

2 _{mean ± SD}

3 _{median + interquartile range}

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Supplementary Table 3. Characteristics of participants with incomplete follow-up compared to

participants with complete follow-up (up until T4).

Variable Complete Follow-up

(n =56,400) Incomplete follow-up(n =29,216)

Demographics

Female1 _55.8 _55.5

Age (years)2 _{44.5 ± 12.2} _{41.2 ± 12.7}

White, East/West European

Ethnicity1 99.0 98.9 Education1 Low 26.2 25.7 Moderate 40.6 41.8 High 33.2 32.5 Lifestyle Body weight (kg)2 _{76.4 ± 12.0} _{76.1 ± 12.2}

Body Mass Index (kg/m2₎2 _{24.7 ± 2.7} _{24.5 ± 2.8}

LifeLines Diet Score2 _{24.2 ± 6.0} _{23.4 ± 6.1}

Energy Intake (kcal/day)2 _{2111 ± 613} _{2087 ± 648}

Alcohol

User percentage1 _86.0 _84.8

Consumption among consumers

(g/day)3 6.5 [2.6 – 12.4] [2.5 – 12.4]6.4 Smoking1 Current Smoker 19.0 25.3 Former Smoker 32.3 27.2 Never Smoker 48.7 47.5 Le i s u re t i m e/C o m m u t i n g Moderate + Vigorous Physical Activity (min/week)3

210

[80-385] [60-385]200

1_%

2 _{mean ± SD}

3 _{median + interquartile range}

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Supplementary Table 4. Males - Age-specific estimated annual weight change in association with

diet quality. Reference quintile 5 of the Lifelines Diet Score represents highest diet quality. Estimates derived from fully adjusted age-stratified linear mixed models. n = 37,930.

Age specific estimates for

diet quality (LLDS) Estimate 95% Confidence Interval fixed effect LLDSP-value

Age 18-29: Lower Upper 0.014

Q1 0.332 0.103 0.562 Q2 0.266 0.034 0.499 Q3 0.161 -0.083 0.405 Q4 0.207 -0.046 0.460 Q5 0 Age 30-39 0.003 Q1 0.169 0.048 0.291 Q2 0.160 0.041 0.279 Q3 0.131 0.007 0.256 Q4 0.014 -0.113 0.141 Q5 0 Age 40-49 0.005 Q1 0.121 0.037 0.206 Q2 0.065 -0.016 0.145 Q3 0.087 0.004 0.170 Q4 0.003 -0.081 0.086 Q5 0 Age 50-59 0.004 Q1 0.155 0.053 0.258 Q2 0.141 0.049 0.233 Q3 0.154 0.061 0.246 Q4 0.070 -0.020 0.160 Q5 0 Age 60-69 0.556 Q1 0.061 -0.070 0.193 Q2 0.079 -0.029 0.187 Q3 0.037 -0.069 0.143 Q4 0.003 -0.098 0.103 Q5 0 Age ≥ 70 0.777 Q1 0.103 -0.185 0.391 Q2 0.024 -0.200 0.248 Q3 -0.005 -0.232 0.221 Q4 -0.066 -0.270 0.138 Q5 0 135558_Petra_Vinke_BNW-def.indd 126 135558_Petra_Vinke_BNW-def.indd 126 3-9-2020 14:33:163-9-2020 14:33:16

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Supplementary Table 5. Females - Age-specific estimated annual weight change in association with

diet quality. Reference quintile 5 of the Lifelines Diet Score represents highest diet quality. Estimates derived from fully adjusted age-stratified linear mixed models. n = 47,686.

Age 18-29: Lower Upper 0.000

Q1 0.219 0.068 0.371 Q2 0.136 -0.014 0.285 Q3 0.160 0.002 0.318 Q4 -0.074 -0.233 0.084 Q5 0 Age 30-39 0.311 Q1 0.054 -0.059 0.166 Q2 0.110 0.006 0.214 Q3 0.077 -0.026 0.181 Q4 0.046 -0.055 0.147 Q5 0 Age 40-49 0.004 Q1 0.141 0.066 0.216 Q2 0.059 -0.005 0.124 Q3 0.061 -0.003 0.125 Q4 0.023 -0.037 0.083 Q5 0 Age 50-59 0.054 Q1 0.070 -0.044 0.185 Q2 0.118 0.036 0.200 Q3 0.080 0.002 0.157 Q4 0.042 -0.024 0.107 Q5 0 Age 60-69 0.688 Q1 -0.060 -0.230 0.110 Q2 -0.026 -0.135 0.084 Q3 -0.054 -0.148 0.040 Q4 0.011 -0.066 0.089 Q5 0 Age ≥ 70 0.029 Q1 -0.444 -0.839 -0.048 Q2 -0.144 -0.368 0.081 Q3 0.131 -0.078 0.340 Q4 -0.101 -0.268 0.066 Q5 0 135558_Petra_Vinke_BNW-def.indd 127 135558_Petra_Vinke_BNW-def.indd 127 3-9-2020 14:33:163-9-2020 14:33:16

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Supplementary Table 6. Males – Age-specific estimated annual weight change in association with

diet quality. Reference quintile 5 of the Lifelines Diet Score represents highest diet quality. Estimates derived from adjusted age-stratified linear mixed models, that is additionally adjusted for change in creatinine. n = 22,989.

Age 18-29: Lower Upper 0.019

Q1 0.321 0.063 0.579 Q2 0.187 -0.074 0.449 Q3 0.130 -0.144 0.405 Q4 0.161 -0.124 0.447 Q5 0 Age 30-39 0.006 Q1 0.188 0.054 0.323 Q2 0.151 0.020 0.282 Q3 0.161 0.024 0.299 Q4 0.027 -0.113 0.166 Q5 0 Age 40-49 0.001 Q1 0.115 0.023 0.207 Q2 0.073 -0.014 0.160 Q3 0.097 0.007 0.187 Q4 -0.029 -0.119 0.061 Q5 0 Age 50-59 0.010 Q1 0.163 0.055 0.272 Q2 0.136 0.040 0.233 Q3 0.146 0.049 0.243 Q4 0.069 -0.026 0.163 Q5 0 Age 60-69 0.362 Q1 0.073 -0.064 0.209 Q2 0.093 -0.018 0.205 Q3 0.008 -0.103 0.118 Q4 0.000 -0.104 0.104 Q5 0 Age ≥ 70 0.529 Q1 0.182 -0.130 0.495 Q2 -0.053 -0.291 0.184 Q3 0.051 -0.191 0.292 Q4 -0.050 -0.269 0.168 Q5 0 135558_Petra_Vinke_BNW-def.indd 128 135558_Petra_Vinke_BNW-def.indd 128 3-9-2020 14:33:173-9-2020 14:33:17

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Supplementary Table 7. Females - Age-specific estimated annual weight change in association with

diet quality. Reference quintile 5 of the Lifelines Diet Score represents highest diet quality. Estimates derived from adjusted age-stratified linear mixed models, that is additionally adjusted for change in creatinine. n = 28,827.

Age 18-29: Lower Upper 0.038

Q1 0.113 -0.069 0.295 Q2 0.093 -0.086 0.273 Q3 0.141 -0.048 0.331 Q4 -0.093 -0.285 0.098 Q5 0 Age 30-39 0.580 Q1 0.032 -0.094 0.159 Q2 0.093 -0.023 0.210 Q3 0.064 -0.052 0.180 Q4 0.043 -0.070 0.157 Q5 0 Age 40-49 0.036 Q1 0.122 0.040 0.203 Q2 0.060 -0.010 0.129 Q3 0.061 -0.007 0.130 Q4 0.020 -0.045 0.084 Q5 0 Age 50-59 0.077 Q1 0.099 -0.025 0.222 Q2 0.120 0.032 0.207 Q3 0.066 -0.018 0.149 Q4 0.034 -0.036 0.104 Q5 0 Age 60-69 0.511 Q1 -0.091 -0.272 0.090 Q2 0.011 -0.104 0.126 Q3 -0.070 -0.169 0.029 Q4 0.004 -0.077 0.084 Q5 0 Age ≥ 70 0.011 Q1 -0.327 -0.740 0.086 Q2 -0.270 -0.499 -0.040 Q3 0.107 -0.099 0.314 Q4 -0.154 -0.325 0.017 Q5 0 135558_Petra_Vinke_BNW-def.indd 129 135558_Petra_Vinke_BNW-def.indd 129 3-9-2020 14:33:173-9-2020 14:33:17

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130 S up pl e m e n ta ry F igur e 2 . A g e -s p e ci fic e st im at e d a n n u al w e ig h t c h an g e in a ss o ci at io n w ith d ie t q u al it y i n A ) m al e s a n d B ) f e m al e s. S e n si tiv it y a n al ys is in 5 1. 8 16 o u t o f 8 5 .6 16 p ar ti ci p an ts , a d d it io n al ly ad ju st in g fo r ch an g e in u ri n ar y cr e at in in e ex cr e ti o n . R e fe re n ce q u in ti le 5 (r ig h t) o f th e L ife lin e s D ie t S co re re p re se n ts h ig h e st d ie t q u al it y. E st im at e s d e ri ved f ro m a g e -s tr at ifi ed li n e ar m ix ed m o d e ls a d ju st ed f o r b as e lin e ed u ca ti o n le ve l, s m o ki n g s ta tu s, e n e rg y i n ta ke , a lc o h o l in ta ke , B M I, l e is u re t im e a n d c o m m u ti n g M V P A a n d t h e ir i n te ra ct io n s w it h t im e , s o t h at b o th i n te rc e p t a n d s lo p e w e re a d ju st e d f o r c o n fo u n d e rs . 135558_Petra_Vinke_BNW-def.indd 130 135558_Petra_Vinke_BNW-def.indd 130 3-9-2020 14:33:173-9-2020 14:33:17

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Supplementary Table 8. Number of participants per quintile of the LLDS, across strata of sex and age. Male

18y-29y 30y-39y 40y-49y 50y-59y 60y-69y ≥70y

LLDS Q1 2536 2384 3048 1076 411 89 LLDS Q2 1637 2378 3579 1494 748 165 LLDS Q3 830 1578 2608 1367 739 167 LLDS Q4 620 1318 2368 1456 864 240 LLDS Q5 239 662 1305 1042 810 172 Female LLDS Q1 2162 1647 2283 585 184 34 LLDS Q2 1885 2124 3518 1270 480 114 LLDS Q3 1251 1950 3412 1452 697 121 LLDS Q4 1230 2017 4048 2316 1175 246 LLDS Q5 829 1614 3892 3033 1795 322

Supplementary Table 9. Number of body weight observations per quintile of the LLDS, across strata

of sex and age.

Male

18y-29y 30y-39y 40y-49y 50y-59y 60y-69y ≥70y

LLDS Q1 6879 6884 9010 3442 1396 281 LLDS Q2 4613 7112 11048 4923 2553 542 LLDS Q3 2390 4714 8176 4576 2537 533 LLDS Q4 1794 3948 7426 4889 2996 804 LLDS Q5 695 2031 4230 3516 2861 567 Female LLDS Q1 6125 4749 6992 1911 601 101 LLDS Q2 5487 6398 11069 4221 1645 369 LLDS Q3 3618 5840 10852 4855 2414 405 LLDS Q4 3534 6161 12990 7871 4099 804 LLDS Q5 2416 4822 12489 10395 6206 1069 135558_Petra_Vinke_BNW-def.indd 131 135558_Petra_Vinke_BNW-def.indd 131 3-9-2020 14:33:173-9-2020 14:33:17

(31)

(32)

Diet quality and cardiometabolic endpoints in

adulthood

(33)