Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: A cohort study

Karl Michaëlsson; John A Baron; Liisa Byberg; Jonas Höijer; Susanna C Larsson; Bodil Svennblad; Håkan Melhus; Alicja Wolk; Eva Warensjö Lemming

doi:10.1371/journal.pmed.1003331

. 2020 Sep 17;17(9):e1003331. doi: 10.1371/journal.pmed.1003331

Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: A cohort study

Karl Michaëlsson ^1,^*, John A Baron ^1,^2,^3,⁴, Liisa Byberg ¹, Jonas Höijer ¹, Susanna C Larsson ^1,⁵, Bodil Svennblad ¹, Håkan Melhus ⁶, Alicja Wolk ^1,⁵, Eva Warensjö Lemming ¹

Editor: Christina Catherine Dahm⁷

PMCID: PMC7497998 PMID: 32941436

Abstract

Background

It is unclear whether the effect on mortality of a higher body mass index (BMI) can be compensated for by adherence to a healthy diet and whether the effect on mortality by a low adherence to a healthy diet can be compensated for by a normal weight. We aimed to evaluate the associations of BMI combined with adherence to a Mediterranean-like diet on all-cause and cardiovascular disease (CVD) mortality.

Methods and findings

Our longitudinal cohort design included the Swedish Mammography Cohort (SMC) and the Cohort of Swedish Men (COSM) (1997–2017), with a total of 79,003 women (44%) and men (56%) and a mean baseline age of 61 years. BMI was categorized into normal weight (20–24.9 kg/m²), overweight (25–29.9 kg/m²), and obesity (30+ kg/m²). Adherence to a Mediterranean-like diet was assessed by means of the modified Mediterranean-like diet (mMED) score, ranging from 0 to 8; mMED was classified into 3 categories (0 to <4, 4 to <6, and 6–8 score points), forming a total of 9 BMI × mMED combinations. We identified mortality by use of national Swedish registers. Cox proportional hazard models with time-updated information on exposure and covariates were used to calculate the adjusted hazard ratios (HRs) of mortality with their 95% confidence intervals (CIs). Our HRs were adjusted for age, baseline educational level, marital status, leisure time physical exercise, walking/cycling, height, energy intake, smoking habits, baseline Charlson’s weighted comorbidity index, and baseline diabetes mellitus. During up to 21 years of follow-up, 30,389 (38%) participants died, corresponding to 22 deaths per 1,000 person-years. We found the lowest HR of all-cause mortality among overweight individuals with high mMED (HR 0.94; 95% CI 0.90, 0.98) compared with those with normal weight and high mMED. Using the same reference, obese individuals with high mMED did not experience significantly higher all-cause mortality (HR 1.03; 95% CI 0.96–1.11). In contrast, compared with those with normal weight and high mMED, individuals with a low mMED had a high mortality despite a normal BMI (HR 1.60; 95% CI 1.48–1.74). We found similar estimates among women and men. For CVD mortality (12,064 deaths) the findings were broadly similar, though obese individuals with high mMED retained a modestly increased risk of CVD death (HR 1.29; 95% CI 1.16–1.44) compared with those with normal weight and high mMED. A main limitation of the present study is the observational design with self-reported lifestyle information with risk of residual or unmeasured confounding (e.g., genetic liability), and no causal inferences can be made based on this study alone.

Conclusions

These findings suggest that diet quality modifies the association between BMI and all-cause mortality in women and men. A healthy diet may, however, not completely counter higher CVD mortality related to obesity.

Karl Michaëlsson and colleagues investigate whether healthier diet can modify the risk of mortality for people with higher body mass index and whether healthier BMI can modify the risk of mortality in people with less healthy diets.

Author summary

Why was this study done?

It is unclear whether the effect on mortality of a higher BMI can be compensated for by adherence to a healthy diet.
It is also unclear whether the effect on mortality by a low adherence to a healthy diet can be compensated for by a normal weight.

What did the researchers do and find?

We conducted a population-based cohort study that included women and men with time-updated lifestyle information.
Obese individuals with high adherence to a Mediterranean-type diet did not experience the increased overall mortality otherwise associated with high BMI, although higher CVD mortality remained.
Lower BMI did not counter the elevated mortality associated with a low adherence to a Mediterranean diet.

What do these findings mean?

Our results indicate that adherence to healthy diets such as a Mediterranean-like diet may modify the association between BMI and mortality.

High body mass index (BMI) accounted for 4.0 million deaths globally in 2015 [1], and more than two-thirds of these deaths were due to cardiovascular disease (CVD) [1]. At middle age, the lowest mortality rates are found in individuals within the higher range (23.5–24.9 kg/m²) of a normal BMI [2–5], but with increasing age, the nadir in mortality is shifted upwards towards those who are modestly overweight [3]. Despite the increasing prevalence of obesity, the rates of CVD-related death continue to decrease in Western societies [6–9], a trend not explained by medical treatment alone [10, 11]. These observations suggest that other factors might modify the higher risk of CVD associated with higher body mass [12]. Potentially, one such factor is diet [13].

Healthy dietary patterns have been associated with lower disease and mortality rates. Several cohort studies [14–18], a secondary prevention trial [19], and one [20] extensively scrutinized [21] primary prevention trial in high-risk individuals have shown inverse associations between adherence to the Mediterranean and Mediterranean-like diets and CVD risk [18, 22]. Moreover, observational studies [23] and randomized trials of a Mediterranean diet [24–30] have generally found beneficial effects on CVD risk factors that are negatively affected by obesity.

Adherence to healthy diets and a normal body weight are emphasized in current dietary recommendations to prolong life [31]. However, whether the effect on mortality of a higher BMI can be compensated for by adherence to a healthy diet is not known. Likewise, whether the impact on mortality of a low adherence to a healthy diet can be compensated for by a normal weight is unclear. We sought to describe the pattern of mortality with cross-classified categories of healthy eating and BMI. With the recommended high adherence to a healthy diet and normal BMI as the reference, we therefore designed a longitudinal analysis to investigate the combined impact of adherence to a Mediterranean-like diet and BMI on all-cause mortality, with CVD mortality as a secondary outcome.

Methods

The study population consisted of participants from 2 population-based cohort studies in Sweden: the Swedish Mammography Cohort (SMC) and the Cohort of Swedish Men (COSM), belonging to the national research infrastructure SIMPLER (www.simpler4health.se). The SMC was established in 1987–1990 when women (born 1914–1948, n = 90,303) residing in 2 counties (Uppsala and Västmanland) were invited to a questionnaire survey covering diet and lifestyle, which was completed by 74% of the women. In the fall of 1997, a second extended questionnaire was sent to all SMC participants who were still alive and residing in the study area (n = 56,030). COSM was established in late 1997 when all male residents (n = 100,303) of 2 counties (Örebro and Västmanland) and born between 1918 and 1952 were invited to participate. When compared with the Official Statistics of Sweden, the cohorts well represented the Swedish population in 1997 in terms of age distribution, educational level, prevalence of overweight and obesity, and smoking status [32]. The 1997 questionnaires in both the SMC and COSM were similar except for the sex-specific questions and included almost 350 items that covered life style factors such as body weight and height, diet (using a validated food frequency questionnaire [FFQ]), dietary supplement use, alcohol consumption, smoking, physical activity, sociodemographic data, and self-perceived health status. This questionnaire was completed by 70% of the women and by 49% of the men. Participants with a prior cancer diagnosis or with energy intakes deemed implausible (±3 SDs from the mean of ln-transformed energy intake) were excluded. The final cohorts consisted of 38,984 women in the SMC and 45,906 men in the COSM followed from January 1, 1998. In 2008, a questionnaire covering general health, lifestyle, and diseases was sent to all participants that had completed the 1997 questionnaire and who were still alive and living in the study area. The response rate was 63% in the SMC and 78% in the COSM. Those who responded to the 2008 questionnaire received an expanded semiquantitative FFQ in 2009; the response rate was 84% and 90% in the SMC and COSM, respectively. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 Checklist). The study has ethical approval by the Regional Ethical Review Boards in Uppsala and Stockholm, Sweden. The questionnaires included a written informed consent. A prespecified analysis plan in Swedish can be found at dx.doi.org/10.17504/protocols.io.bgftjtnn and in S1 Protocol in Swedish and with English translation.

BMI

We categorized BMI into normal (20 to <25 kg/m²), overweight (25 to <30 kg/m²), and obese (≥30 kg/m²), using self-reported weight and height in 1997 and 2008. Overall, 4% of data points were missing. Those with a BMI below 20 kg/m² at baseline were excluded (n = 3,226, 4%; 2,354 women and 872 men) since a low body mass can reflect frailty or prevalent disease, which were not intended to be examined in this analysis. Therefore, our final data set used for the analyses contains 79,003 women (44%) and men (56%).

Modified Mediterranean-like diet (mMED) score

The dietary assessment has been described previously [33]. Briefly, the FFQs included 96 and 132 food items in 1997 and 2009, respectively. We calculated an mMED score adapted from the Mediterranean diet scale devised by Trichopoulou and colleagues [34] using previously defined food items [35], but the scoring was modified according to Knudsen and colleagues, rendering a continuous score [36]. Details of the scoring, ranging from 0 to 8 on a continuous scale, are found below; participants with higher score points were more adherent to the diet. For analysis, mMED was classified into 3 predefined categories (0 to <4, 4 to <6, and 6–8 score points) chosen to balance exposure range and numbers of individuals in each category [37].

Participants indicated in the FFQs how often, on average, they had consumed each food item during the last year, choosing from 8 predefined frequency categories ranging from "never/seldom" to "3 or more times per day". Frequently consumed foods such as dairy products and bread were reported as the number of servings per day (open question). Information on fat type including vegetable oils used in cooking and as salad dressing was also reported. At baseline in 1997, 19% of the women and 12% of the men reported use of olive oil in dressing. The corresponding frequencies were 25% and 19% for use of olive oil in cocking. In 2009, 41% of the women and 37% of the men reported use of olive oil in dressing and with similar proportions of olive oil use in cocking. Total amount of alcohol consumed per day was derived from the FFQ by multiplying the reported frequencies with the reported amounts on a single occasion. Energy intake was estimated by multiplying the portion-specific consumption frequency of each food item with the nutrient content obtained from the Swedish food database [33].

The mMED score comprises 8 components: fruit and vegetables (apple, banana, berry, orange/citrus, and other fruit; carrot, beetroot, broccoli, cabbage, cauliflower, lettuce, onion, garlic, pepper, spinach, tomato, and other vegetables), legumes (peas, lentils, beans, and pea soup) and nuts, unrefined or high-fiber grains (whole-meal bread, crisp bread, oatmeal, and bran of wheat), fermented dairy products (sour milk, yoghurt, and cheese), fish (excluding shellfish), red and processed meat, any use of olive or rapeseed oil for cooking or as dressing, and alcohol intake.

An individual with a reported intake above or below a specific cut point for each component of a diet score usually receives discrete score points (0 or 1), but in the method of Knudsen and colleagues [36], each individual receives 1 or a ratio between the actual intake and a chosen intake amount. Such an approach generates continuous component variables and improves precision of the exposure assessment. In the present study, the reference points for fruit and vegetables, legumes and nuts, nonrefined or high-fiber grains, fermented dairy products, and fish were the median intakes in the 1997 data and are lower-intake thresholds. A participant with an intake of legume and nuts of x grams will thus receive the score = x/median₁₉₉₇. For red and processed meat, consumption below the population median intake rendered a score of 1 point, intakes of 2 or more times the population median rendered 0 points, and intakes above the median (but below 2 × population median) rendered a score of 1 − (actual intake − median₁₉₉₇)/median₁₉₉₇. Any use of olive or rapeseed oil gave 1 point and otherwise 0 points. The alcohol component was coded as intake divided by 5 in the range 0–5 grams/day, as 1 in the intake range 5–15 grams/day, as 1 × (intake − 15)/15 in the range above 15 up to 30 gram/day, and 0 for intakes above 30 grams/day.

The same 1997 cutoff points were applied using the 2009 data in order to avoid secular trends and intake differences caused by the fact that the number of food items was higher in the 2009 FFQ. The more detailed FFQ in 2009 is a reflection of a greater diversification of diet over time.

Assessment of covariates

Covariates obtained from the questionnaires (1997 and 2008/2009) were age, smoking status (including cigarettes per day at different ages), walking/cycling, leisure time, physical exercise during the past year, and, as markers of socioeconomic status, cohabiting/marital status as well as educational level. The exercise questions have been validated against activity records and accelerometer data [38]. Comorbidity, expressed as Charlson’s weighted comorbidity index [39, 40], was defined using ICD diagnosis codes (versions 8, 9, and 10) from the National Patient Register from 1964 to before baseline 1 January 1998. Information on diabetes mellitus was retrieved from the questionnaire and from the National Patient Register.

Assessment of deaths

All-cause mortality was our primary outcome, with information obtained from the continuously updated Swedish Total Population Register. A complete linkage with the register is possible since all Swedish residents have a unique personal identity number. Since 1952, the National Board of Health and Welfare has maintained information with yearly updates on the causes of death for all Swedish residents in the Cause of Death Registry. We used the underlying cause of death to define our secondary outcome, mortality from CVD (ICD-10 codes I00–I99).

Statistical analysis

For each participant, follow-up time accrued from 1 January 1998 until the date of death, a questionnaire response indicating a BMI <20 kg/m² in 2008 (n = 1,824), or the end of the study period (31 October 2018 for all-cause mortality and 31 December 2017 for CVD mortality). The associations of mMED and BMI with all-cause mortality and CVD mortality were assessed as age and multivariable-adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) by Cox proportional hazards regression models, with time-updated information of all variables except Charlson’s comorbidity index and diabetes mellitus (defined only at baseline) and calendar date as the timescale. Both exposures were initially treated as continuous variables. To select suitable covariates for the multivariable model, we used current knowledge and a directed acyclic graph [41], presented as S2 Protocol. The overall model included sex, age (splines with 3 knots), educational level (≤9, 10–12, >12 years, other), living alone (yes or no), leisure time exercise during the past year (<1 h/w, 1 h/w, 2–3 h/w, 4–5 h/w, >5 h/w), walking/cycling (almost never, <20 min/d, 20–40 min/d, 40–60 min/d, 1–1.5 h/d, >1.5 h/d), height (splines with 3 knots), energy intake (splines with 3 knots), smoking habits (current, former, never), Charlson’s weighted comorbidity index (continuous), and diabetes mellitus as a separate marker variable (yes/no). Missing data were imputed (20 imputations) using Stata’s “mi” package (multiple imputations using chained equations). The proportion of missing data in the cohorts was 4% for BMI, 3% for height, 9% for walking/bicycling, 11% for exercise, and 6% for marital status. For all other covariates, the percentage of missing was less than 2%. Missingness of foods at baseline was for fruit and vegetables 0.1%, legumes and nuts 3.4%, grains 0.9%, fermented dairy products 2.9%, fish 1.1%, meat 0.7%, olive or rapeseed oil 0%, and alcohol intake 0%.

Nonlinear trends of mortality were assessed using restricted cubic splines with 3 knots placed at centiles 10, 50, and 90 of mMED and BMI, respectively. We performed stratified analyses in subgroups of potential confounders in which BMI below or above the median of 26 kg/m² and mMED score were examined as continuous variables. The purpose of these analyses in homogeneous strata was 2-fold: to visualize and evaluate potential confounding, although with a limitation of different baseline hazards in the strata, and to evaluate potential effect modification of the exposures.

Combinations of the categories of BMI and mMED were used to jointly classify study participants into 9 strata. Participants with normal BMI and in the highest category of mMED were used as the reference category in these analyses. Test for homogeneity of HRs across strata was done according to Fleiss [42]. We conducted a stratified analysis by sex with all-cause mortality as outcome. A complementary analysis of risk differences (RDs) was suggested by one of the reviewers. By using the approach described by Austin [43], multivariable-adjusted RDs and relative risks (RRs) were calculated from the predicted survival curve based on the Cox model for all-cause mortality. For the main analysis, the RDs and RRs (with 95% CIs based on 500 bootstrap replicates) were calculated at 20 years, and for the sensitivity analysis starting follow-up in 2009, RDs and RRs at 9 years of follow-up were calculated. For the sensitivity analysis, in which no variable information was updated, yet another method based on pseudo-observations [44], as suggested by the reviewer, was used to estimate the RDs.

Additional sensitivity analyses were conducted, excluding those with a BMI greater than 35 kg/m², restricting those with normal BMI to 22–25 kg/m², adding adjustment for pack-years of smoking, restricting analysis to never-smokers, excluding those with pre-existing diseases before baseline (chronic obstructive lung disease, cancer, myocardial infarction or other ischemic heart disease, heart failure, peripheral arterial disease, and stroke as suggested by the reviewer), and excluding the first 2 years of follow-up.

Statistical analyses were carried out in Stata version 15.1 (StataCorp, College Station, TX, USA) and in R, version 4.0 (R Core Team, 2020).

Results

Age-standardized baseline characteristics (mean age 61 years, range 45–83) within categories of BMI and mMED are displayed in Table 1. Ten percent of the participants were obese, and 46% had a normal BMI; 44% of the participants reported dietary habits consistent with high adherence to mMed and 8% low adherence. Individuals who were overweight or obese reported lower educational attainment, a higher prevalence of diabetes, and less exercise than those with a normal BMI. Those with high mMED had higher educational attainment, higher physical activity level and energy intake, and a higher prevalence of cohabitation.

Table 1. Age-standardized baseline characteristics of the participants by 3 categories of BMI and 3 categories of Mediterranean diet score, respectively.

		BMI (kg/m²)			Mediterranean Diet Score (Score Points)
		20.0–24.9	25.0–25.9	30 or more	0 to <4	4 to <6	6–8
		n = 36,065	n = 31,850	n = 8,129	n = 6,256	n = 38,063	n = 34,684
Female, n (%)		17,930 (49.7%)	12,124 (38.1%)	3,808 (46.8%)	2,429 (38.8%)	15,527 (40.8%)	16,326 (47.1%)
Male, n (%)		18,135 (50.3%)	19,726 (61.9%)	4,321 (53.2%)	3,827 (61.2%)	22,536 (59.2%)	18,358 (52.9%)
Age, mean (SD)		61 (9)	61 (9)	61 (9)	61 (10)	61 (9)	61 (9)
Education, n (%)	<10 years	24,272 (67.6%)	23,534 (74.3%)	6,455 (79.8%)	5,175 (83.6%)	29,002 (76.7%)	22,502 (72.1%)
	10–12 years	4,227 (11.8%)	3,458 (10.9%)	691 (8.6%)	478 (7.7%)	3,902 (10.2%)	4,273 (12.4%)
	>12 years	7,415 (20.6%)	4,683 (14.8%)	940 (11.6%)	537 (8.7%)	4,929 (13.0%)	7,785 (22.5%)
Height, cm (SD)		171 (9)	172 (9)	170 (9)	171 (9)	172 (9)	172 (9)
BMI (kg/m²)		23 (1.3)	27 (1.4)	33 (2.8)	26 (3.8)	26 (3.5)	25 (3.3)
BMI categories (kg/m²)	20–24.9	na	na	na	2,392 (41.6%)	16,419 (45.0%)	17,234 (51.0%)
	25.0–29.9	na	na	na	2,434 (42.3%)	15,797 (43.3%)	13,654 (40.4%)
	30 or more	na	na	na	929 (16.1%)	4,298 (11.8%)	2,926 (8.7%)
Living alone, n (%)		6,714 (20.0%)	5,548 (18.4%)	1,793 (23.6%)	1,822 (31.1%)	7,516 (20.9%)	5,355 (16.5%)
Energy intake, kcal/day (median, IQR)		2,086 (1,620, 2,702)	2,156 (1,659, 2,775)	2,041 (1,562, 2,652)	1,700 (1,221, 2,331)	2,077 (1,586, 2,713)	2,222 (1,764, 2,808)
Charlson comorbidity index, n (%)	0	31,914 (88.5%)	27,751 (87.1%)	6,825 (84.0%)	5,206 (83.2%)	33,073 (86.9%)	30,725 (88.6%)
	1	3,822 (10.6%)	3,745 (11.8%)	1,181 (14.5%)	935 (15.0%)	4,554 (12.0%)	3,667 (10.6%)
	2 or more	329 (0.9%)	354 (1.1%)	123 (1.5%)	115 (1.8%)	436 (1.2%)	292 (0.9%)
Diabetes mellitus, n (%)	No	34,277 (95.0%)	29,354 (92.2%)	7,027 (86.4%)	5,669 (90.6%)	35,045 (92.1%)	32,531 (93.8%)
	Yes	1,788 (5.0%)	2,496 (7.8%)	1,102 (13.6%)	587 (9.4%)	3,018 (7.9%)	2,153 (6.2%)
Smoking status, n (%)	Current use	8,957 (25.2%)	6,974 (22.2%)	1,755 (21.8%)	2,042 (33.6%)	9,481 (25.3%)	7,046 (20.6%)
	Former use	10,233 (28.8%)	10,955 (34.9%)	2,907 (36.2%)	1,576 (25.9%)	11,474 (30.6%)	12,009 (35.1%)
	Never use	16,311 (46.0%)	13,455 (42.9%)	3,377 (42.0%)	2,462 (40.5%)	16,503 (44.1%)	15,193 (44.4%)
Exercise, n (%)	<1 h/w	5,875 (18.1%)	6,061 (21.2%)	2,188 (30.8%)	1,731 (34.2%)	8,087 (23.9%)	5,000 (15.7%)
	1 h/w	6,397 (19.8%)	6,354 (22.2%)	1,666 (23.5%)	976 (19.3%)	7,204 (21.3%)	6,677 (21.0%)
	2–3 h/w	11,060 (34.0%)	9,158 (32.1%)	1,923 (27.1%)	1,316 (26.0%)	10,346 (30.6%)	11,221 (35.2%)
	4–5 h/k	4,405 (13.5%)	3,406 (11.9%)	656 (9.3%)	475 (9.4%)	3,888 (11.5%)	4,392 (13.8%)
	6 h or more/w	4,792 (14.7%)	3,576 (12.5%)	662 (9.3%)	570 (11.2%)	4,290 (12.7%)	4,563 (14.3%)
Walking or cycling, n (%)	Almost never	3,209 (9.7%)	3,870 (13.3%)	1,501 (20.5%)	1,119 (21.1%)	4,939 (14.3%)	2,934 (9.1%)
	<20 min/d	6,461 (19.5%)	6,866 (23.5%)	1,889 (25.8%)	1,183 (22.3%)	7,971 (23.1%)	6,680 (20.6%)
	20–40 min/d	10,992 (33.2%)	9,083 (31.1%)	2,001 (27.4%)	1,436 (27.1%)	10,360 (30.0%)	10,978 (33.9%)
	40–60 min/d	6,097 (18.4%)	4,672 (16.0%)	925 (12.6%)	704 (13.3%)	5,538 (15.5%)	5,974 (18.5%)
	1–1.5 h/d	3,531 (10.7%)	2,519 (8.6%)	558 (7.6%)	408 (7.7%)	3,212 (9.3%)	3,247 (10.0%)
	>1.5 h/d	2,848 (8.6%)	2,154 (7.4%)	437 (6.0%)	449 (8.5%)	2,727 (7.9%)	2,529 (7.8%)
Mediterranean diet score, (median, IQR)		5.9 (5.1, 6.7)	5.8 (5.0, 6.6)	5.6 (4.7, 6.4)	3.5 (3.1, 3.8)	5.3 (4.8, 5.6)	6.7 (6.3, 7.1)
Mediterranean diet score, (units)	0–4	2,422 (6.7%)	2,427 (7.6%)	908 (11.2%)	na	na	na
	5 to <6	16,439 (45.6%)	15,794 (49.6%)	4,288 (52.7%)	na	na	na
	6–8	17,204 (47.7%)	13,629 (42.8%)	2,933 (36.1%)	na	na	na

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Abbreviations: BMI, body mass index; na, not applicable.

During up to 21 years of follow-up (mean 17.4 years) that accrued 1,372,266 person-years of observation, 30,389 (38%) participants died (22 deaths per 1,000 person-years). HRs of death were related to BMI in a J-shaped pattern (Fig 1A for all-cause mortality and Fig 1B for cardiovascular mortality) and inversely with adherence to mMED (Fig 1C for all-cause mortality and Fig 1D for cardiovascular mortality). The nadir in HRs of all-cause mortality was around a BMI of 26 kg/m², the median, with an HR of 1.022 (95% CI 1.017–1.027) per 1 kg/m² above this level. Each unit higher mMED score was associated with a multivariable-adjusted HR of 0.860 (95% CI 0.849–0.871).

Fig 1 — The dark gray shaded regions in the figures correspond to 95% CIs, and the spike plots represent the distribution of BMI and mMED scores, respectively. Assessed by multivariable-adjusted HRs using of Cox regression analysis and restricted cubic splines, with a BMI of 25 kg/m² and mMED score of 8 units as references. HRs adjusted for sex, age (splines with 2 knots), educational level (≤9, 10–12, >12 years, other), living alone (yes or no), leisure time physical exercise during the past year (<1 h/w, 1 h/w, 2–3 h/w, 4–5 h/w, >5 h/w), walking/cycling (almost never, <20 min/d, 20–40 min/d, 40–60 min/d, 1–1.5 h/d, >1.5 h/d), height (splines with 2 knots), energy intake (splines with 2 knots), smoking habits (current, former, never), Charlson’s weighted comorbidity index (continuous; 1–16), and diabetes mellitus (yes/no). BMI, body mass index; CI, confidence interval; CVD, cardiovascular disease; HR, hazard ratio; mMED, modified Mediterranean-like diet.

Fig 2A and Fig 2B illustrate the analysis of mortality by categories of covariates for BMI as a continuous variable (in subgroups below or above the median of 26 kg/m² to take into account the J-shaped association with risk) and for mMED score as a continuous variable. The overall pattern of the HRs for all-cause mortality were in the same direction within each subgroup.

Associations of cross-classified categories of BMI and mMED with total mortality are illustrated in Fig 3A, using normal BMI (mean 23 kg/m²) and high mMED as the reference. We found the lowest mortality among overweight (mean 27 kg/m²) individuals with high mMED (HR 0.94; 95% CI 0.90, 0.98). Whatever the BMI category, a high mMED score brought the point estimate of the HR to the reference level or below. In particular, obese individuals (mean BMI 33 kg/m²) with high mMED scores did not have significantly elevated HR of all-cause mortality (HR of 1.03; 95% CI 0.96–1.11). In contrast, lower BMI did not compensate for a low mMED score. No matter what the BMI, participants with a low mMED score retained an elevated risk. Indeed, participants with a normal BMI but a low mMED score had an overall mortality HR of 1.60 (95% CI 1.48–1.74), which was actually higher than that for obese individuals with high mMED (p < 0.0001 for homogeneity). We found similar estimates among women and men (Table 2) as in the pooled analysis (Fig 3A). The attenuation of the estimates after multivariable adjustment was mainly driven by differences in physical activity.

Table 2. Combined associations of a Mediterranean diet score and BMI on all-cause mortality in women and in men.

High adherence to a Mediterranean diet and a normal body index is the reference with an HR of 1.0.

Women			BMI (kg/m²)
			20.0–24.9 (mean 23)	25.0–25.9 (mean 27)	30 or more (mean 33)
Mediterranean diet score, (score points)	6–8 (median 6.8)	Age-adjusted HR	1.0 (ref)	0.98 (0.92–1.06)	1.17 (1.05–1.30)
Mediterranean diet score, (score points)	6–8 (median 6.8)	Multivariable-adjusted^* HR	1.0 (ref)	0.92 (0.86–0.99)	0.99 (0.88–1.10)
	4 to <6 (median 5.2)	Age-adjusted HR	1.59 (1.49–1.69)	1.42 (1.33–1.52)	1.67 (1.54–1.82)
	4 to <6 (median 5.2)	Multivariable-adjusted^* HR	1.46 (1.37–1.55)	1.24 (1.16–1.33)	1.31 (1.21–1.43)
	0 to <4 (median 3.5)	Age-adjusted HR	1.99 (1.76–2.26)	1.87 (1.64–2.12)	1.94 (1.63–2.31)
	0 to <4 (median 3.5)	Multivariable-adjusted^* HR	1.67 (1.47–1.89)	1.53 (1.35–1.74)	1.46 (1.22–1.74)
Men			BMI (kg/m²)
			20.0–24.9 (mean 23)	25.0–25.9 (mean 27)	30 or more (mean 33)
Mediterranean diet score, (score points)	6–8 (median 6.6)	Age-adjusted HR	1.0 (ref)	1.01 (0.95–1.06)	1.30 (1.19–1.42)
Mediterranean diet score, (score points)	6–8 (median 6.6)	Multivariable-adjusted^* HR	1.0 (ref)	0.94 (0.89–0.99)	1.06 (0.97–1.16)
	4 to <6 (median 5.3)	Age-adjusted HR	1.41 (1.34–1.48)	1.33 (1.26–1.40)	1.69 (1.58–1.81)
	4 to <6 (median 5.3)	Multivariable-adjusted^* HR	1.32 (1.26–1.39)	1.17 (1.11–1.24)	1.33 (1.24–1.43)
	0 to <4 (median 3.5)	Age-adjusted HR	1.85 (1.68–2.03)	1.61 (1.46–1.77)	2.05 (1.76–2.39)
	0 to <4 (median 3.5)	Multivariable-adjusted^* HR	1.57 (1.42–1.74)	1.28 (1.16–1.41)	1.52 (1.31–1.77)

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*Adjusted by age (splines with 2 knots), educational level (≤9, 10–12, >12 years, other), living alone (yes or no), leisure time physical exercise during the past year (<1 h/w, 1 h/w, 2–3 h/w, 4–5 h/w, >5 h/w), walking/cycling (almost never, <20 min/d, 20–40 min/d, 40–60 min/d, 1–1.5 h/d, >1.5 h/d), height (splines with 2 knots), energy intake (splines with 2 knots), smoking habits (current, former, never), Charlson’s weighted comorbidity index (continuous; 1–16), and diabetes mellitus (yes/no). Abbreviations: BMI, body mass index; HR, hazard ratio.

RDs and RRs are presented in S1 Table. Generally, the results followed the same pattern as that for the HRs. At 20 years of follow-up, the mortality risk difference for participants with a normal BMI and a low mMED score compared with those with a high mMED score and a normal BMI was 0.094 (95% CI 0.090–0.097), corresponding to a number needed to treat of 11 individuals.

Our secondary outcome was CVD mortality, with 12,064 cardiovascular deaths during follow-up (Fig 3B). For this outcome, the lowest mortality HRs were in participants with high mMED scores and normal or overweight BMI (Fig 3B). A high mMED score was associated with lower CVD mortality within each BMI stratum, but in contrast to findings for total mortality, individuals with high mMED scores and obesity retained a modestly elevated HR of 1.29 (95% CI 1.16–1.44). Otherwise, the patterns of the HRs were similar to those for all-cause mortality. Participants with a normal BMI but low mMED score had a CVD mortality HR of 1.76 (95% CI 1.55–1.99), which was statistically indistinguishable from the HR for the obese.

Further sensitivity analyses revealed similar estimates as the primary analyses for the combined exposures of BMI and mMED, including exclusion of participants with a BMI higher than 35 kg/m² (S1 Fig), restricting the analysis to those with normal BMI to a more narrow 22–25 kg/m² range (S2 Fig), additionally adjusting for pack-years of smoking (S3 Fig), restricting analysis to never-smokers (S4 Fig), and excluding individuals with any of the following criteria: ever smokers, BMI below 22 kg/m², and those with pre-existing diseases before baseline (chronic obstructive lung disease, cancer, myocardial infarction or other ischemic heart disease, heart failure, peripheral arterial disease, and stroke), as well as excluding the first 2 years of follow-up (S5 Fig).

Discussion

In this large, population-based cohort analysis of middle-aged and older men and women, obese individuals with high adherence to a Mediterranean-type diet did not experience the increased overall mortality otherwise associated with high BMI, although a higher CVD mortality remained. However, lower BMI did not appear to counter the elevated mortality associated with a low adherence to a Mediterranean-like diet: individuals with a low mMED score retained an increased mortality even with a normal BMI. These results indicate that adherence to healthy diets such as a Mediterranean-like diet may be a more appropriate focus than avoidance of obesity for the prevention of overall mortality.

Ours is the first large cohort study examining the combined association of BMI and a Mediterranean-like diet with rates of mortality. The novelty of our study is the examination of combined strata of BMI and Mediterranean diet. The J-shaped association between BMI and all-cause as well as cardiovascular mortality confirms results from previous observational studies [2–5] as well as a mendelian randomization study based on a genetic instrument for BMI [45]. A modestly sized secondary prevention trial of a Mediterranean diet after a myocardial infarction reported more than a halved rate of all-cause mortality after 4 years among those randomized to the diet [19].

Our results are also partially consistent with those of the larger primary prevention PREDIMED trial. This study included 7,447 participants 55–80 years of age with a mean BMI of 30 kg/m² at high risk of CVD [20]. After 5 years of follow-up, there was a 30% reduction in risk of myocardial infarction, stroke, or cardiovascular death in those randomized to a Mediterranean diet, with an even larger effect in obese individuals. However, all-cause mortality was not affected by the intervention [20]. The magnitude of differences in the 14-point score between the Mediterranean diet intervention and the control diet group during different time points of follow-up in the PREDIMED trial was not large, ranging from 1.4 to 1.8 points, a smaller exposure contrast than in our study.

Potential mechanisms

A high BMI has been associated with a negative impact on risk factors for premature death and CVD, including hypertension, insulin resistance, hyperlipidemia, low-grade inflammation, and oxidative stress [46]. In contrast, intervention studies have shown reduced blood pressure, improved insulin resistance, lower blood lipids, and lower inflammation and oxidative stress marker levels with Mediterranean-like diet even in those with continuing high body weight [47–53]. Additionally, these diets have effects on gut-microbiota–mediated production of metabolites influencing metabolic health [49], higher circulating adiponectin concentrations [52], and improved endothelial function [52]. Even though a Mediterranean-like diet seems to have counteracted higher all-cause mortality associated with obesity in our study, these individuals still had modestly higher CVD mortality, albeit with lower rates than obese individuals who had lower mMED scores. This remaining elevation in risk could have several different explanations; one might be the consequence of a common genetic predisposition to both high BMI and CVD [54–56]. Another biological explanation may be that an even higher adherence to a classical Mediterranean diet is needed to fully compensate for obesity or that the negative effect of obesity on cardiovascular risk factors cannot be fully compensated for by healthy eating.

The relatively high mortality rates in our study among individuals with a normal weight, even among never-smokers, might seem counterintuitive. However, nutritional reserves may be particularly needed at older ages, and sarcopenia associated with low body weight and malnutrition is a strong independent predictor of early death [57, 58]. A healthy diet, including a Mediterranean diet, is related to a lower future risk of sarcopenia, frailty, and falls [59–62]. A low BMI and a low adherence to mMED are both strongly associated with higher risk of fragility fractures [63–65], which in turn leads to high mortality rates [66, 67]. In elderly individuals, concomitant low BMI and malnutrition have also led to decreased immune function, followed by a higher risk of infections [68, 69] and higher risk of surgical complications [70], more frequent hospital admissions, and a 4-fold greater risk of mortality [71].

Strengths and limitations

Our analysis was made possible by use of 2 population-based cohorts in a setting with wide variation in dietary habits. We had a long follow-up with a large number of deaths, ascertained by use of national register information and personal identification numbers without loss to follow-up. We used time-updated information on diet, other lifestyle factors such as exercise and walking, socioeconomic status, and comorbidity information in our statistical analysis. Exclusion of very lean individuals from the analysis lowered the risk of reverse causation bias. The results were independent of other major known risk factors for early death, and we found consistency of the HRs in subgroups of covariates, an indication of no major confounding or effect modification. However, our results might not apply to people in other settings with different dietary patterns, to those with more extreme obesity (BMI >35 kg/m²), or to younger age groups. Measurement errors in self-reported lifestyle factors such as the diet are inevitable, generally leading to conservatively biased estimates of association. Although recall of weight and height on average are quite accurate, those with high body weight tend to slightly underreport their weight [72], and therefore, some truly obese individuals might have been classified as overweight. Most importantly, our observational study of the associations of diet and BMI with mortality cannot prove that weight loss or dietary change can reduce the risk of death, and therefore, our RDs and corresponding numbers needed to treat are recommended to be cautiously interpreted. Clinical trials would be required for that level of certainty, but long-term adherence to the allocated diet is an issue with such design. Replication of our results by independent researchers and with use of other cohorts with time-updated lifestyle information would also be of additional value since recommendations cannot be based on our findings alone.

Conclusions

The results from this longitudinal cohort study indicate that for both women and men during the last decades of life, diet can modify the association of a higher BMI with mortality; obese individuals adhering to a Mediterranean diet did not have an increased mortality in comparison to more lean individuals. In contrast, a lean BMI did not offset a poor diet. Nonetheless, a healthy diet may not completely counter higher CVD mortality related to obesity.

Supporting information

S1 STROBE Checklist. Checklist according to STROBE guidelines.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(30.4KB, docx)}

S1 Protocol. Prospective study plan.

(DOCX)

Click here for additional data file.^{(26.7KB, docx)}

S2 Protocol. Directed acyclic graph with code displaying the selection of covariates for the analysis of association of BMI combined with adherence to a Mediterranean-like diet with mortality.

BMI, body mass index

(DOCX)

Click here for additional data file.^{(945.9KB, docx)}

S1 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after exclusion of those with BMI higher than 35 kg/m².

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(149.8KB, docx)}

S2 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after restriction of the analysis to those with normal BMI to a more narrow 22–25 kg/m² range.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(141.9KB, docx)}

S3 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after extending the multivariable model by additional adjustment for pack-years of smoking.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(149.8KB, docx)}

S4 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after restriction to never-smokers.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(144.1KB, docx)}

S5 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality excluding individuals with any of the following criteria: Ever smokers, BMI below 22 kg/m², and those with pre-existing diseases before baseline (chronic obstructive lung disease, cancer, myocardial infarction or other ischemic heart disease, heart failure, peripheral arterial disease, and stroke) and excluding the first 2 years of follow-up.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(146.7KB, docx)}

S1 Table. Associations of combinations of BMI and adherence to an mMED with all-cause mortality.

The upper part of the table presents results with use of time-updated information and 20 years of follow-up from 1997 and the lower part with use of 9 years of follow-up from 2009. The estimated associations are all multivariable-adjusted*. Absolute RDs and RRs (at 20 years and 9 years of follow-up, respectively) are calculated from the predicted survival curves based on the multivariable-adjusted Cox model. The last column of the 9 years follow-up from 2009 presents absolute RDs calculated from pseudo-observations using a GEE model with identity link. BMI, body mass index; GEE, generalized estimated equation; mMED, modified Mediterranean-like diet; RD, risk difference; RR, relative risk.

(DOCX)

Click here for additional data file.^{(15.3KB, docx)}

Acknowledgments

We acknowledge the Swedish Research Council-supported national research infrastructure SIMPLER for provisioning of facilities and experimental support, and we thank Anna-Karin Kolseth for her assistance. The computations were performed on resources provided by the Swedish National Infrastructure for Computing’s (https://www.snic.se/) support for sensitive data (SNIC-SENS) through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SIMP2019004.

Abbreviations

BMI: body mass index
CI: confidence interval
COSM: Cohort of Swedish Men
CVD: cardiovascular
FFQ: food frequency questionnaire
HR: hazard ratio
mMED: modified Mediterranean-like diet
RD: risk difference
RR: relative risk
SMC: Swedish Mammography Cohort
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

Data Availability

Data cannot be shared publicly because of the sensitive nature of the data and the GDPR legislation. Data are available from the national research infrastructure SIMPLER for researchers who meet the criteria for access to confidential data. Details of how to obtain data from the national research infrastructure SIMPLER can be obtained at the website www.simpler4health.se. Our study has the SIMPLER project reference SIMP2019004.

Funding Statement

The study was supported by grants from the Swedish Research Council (https://www.vr.se; grant nos. 2015-03257, 2017-00644, and 2017-06100 to KM). The national research infrastructure SIMPLER receives funding through the Swedish Research Council under the grant no. 2017-00644 (to Uppsala University and KM). The computations were performed on resources provided by the Swedish National Infrastructure for Computing’s (www.snic.se) support for sensitive data SNIC-SENS through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project SIMP2019004. SNIC is financially supported by the Swedish Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003331.r001

Decision Letter 0

Louise Gaynor-Brook

14 Mar 2020

Dear Dr Michaëlsson,

Thank you for submitting your manuscript entitled "Combined impact of body mass index and adherence to a Mediterranean-like diet on all-cause and cardiovascular mortality: a cohort study in women and men" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Louise Gaynor-Brook, MBBS PhD

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1003331.r002

Decision Letter 1

Emma Veitch

2 May 2020

Dear Dr. Michaëlsson,

Thank you very much for submitting your manuscript "Combined impact of body mass index and adherence to a Mediterranean-like diet on all-cause and cardiovascular mortality: a cohort study in women and men" (PMEDICINE-D-20-00848R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We expect to receive your revised manuscript by May 25 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Emma Veitch, PhD

PLOS Medicine

On behalf of Clare Stone, PhD, Acting Chief Editor,

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

*In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

*Due to the observational design of the study, we'd suggest care in using causal language - at the moment the abstract uses language such as "reduce(s) risk..", which may be better phrased as "associated with a reduction in risk". There may be other places to modify this too.

*Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

*We'd suggest ensuring that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (SChecklist)." The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

-----------------------------------------------------------

Comments from the reviewers:

Reviewer #1: Statistical review

This paper reports the analysis of a cohort study investigating association between diet (as quantified with a Mediterranean diet score) and BMI on mortality. The follow-up is long and the sample size is high; I also thought generally the methods and reporting was good. I have some minor comments:

1. Abstract - "Obese individuals with high mMED did not experience

higher rates of all-cause mortality" - add something along the lines of 'there was no significant evidence of..' or 'did not experience significantly higher…'

2. Given the questionnaire completion was moderate, especially in men, can anything be added on how representative those who completed the questionnaire are of the general population?

3. Page 9 "Covariates, chosen using directed acyclic graphs," - can more information be given on how they were selected? Did this use any data?

4. Page 9 -how much missing data was there was on the covariates (it would be sufficient to say what proportion were complete cases)?

5. Page 9 - "We performed additional analyses in subgroups of potential confounders in which BMI below or above the median of 26 kg/m2 and mMDS were included as continuous variables.": I didn't follow this; what were the confounders, were they pre-specified and is this a stratified analysis or something different? It would be useful if an analysis plan was included if this was pre-specified.

6. Page 11 - the results of the sensitivity analyses should be provided in supplementary material (as per PLOS medicine guidance prohibiting 'data not shown').

James Wason

-----------------------------------------------------------

Reviewer #2: This well-written manuscript presents the results of individual analyses of BMI and a Mediterranean diet score, as well as a joint analysis of these, in relation to all-cause and CVD mortality in a Swedish cohort of older adults. The work has many strengths; here I will detail some areas in which I believe that it could be improved. In particular, I have grappled with what the purpose of the study is, what the results may be telling us, and what they can be used for.

Major comments:

1. The authors present a 3x3 table, detailing a joint analysis of two exposures on the multiplicative scale. This is in essence an interaction analysis, and I commend it. It appears that there is no interaction between BMI and diet quality, and that diet quality alone associates with greater all-cause mortality. The authors conclude that it is more important to focus on improving diet quality than preventing obesity to lower all-cause mortality in this age group. However, the individual analyses of BMI and diet show that each of these exposures are associated with all-cause mortality. In the absence of a multiplicative interaction, there must be an interaction on the additive scale. What are the implications of this for public health messages? Ought public health interventions to be targeting diet alone, which the conclusions of the abstract indicate should be the case? Where can most lives be saved - among those with poor diets or those who are of an unhealthy BMI (see also comment below)? If this is the purpose of the study, I would recommend redoing the analyses using methods that estimate risk differences. The pseudoobservation method can be used for time-to-event data; others also exist. (see https://doi.org/10.1186/1471-2288-14-97 ; https://doi.org/10.1515/em-2017-0015) If the intention is to assess multiplicative interactions, please be mindful of how these are greatly dependent on the baseline hazard of death, and consider this in the interpretation of the results. Also, the effects of time varying exposures on time-varying confounders, and any feedback loops between these, cannot be estimated without bias using Cox proportional hazards. In this case, g computation is preferable, as this bias can be taken into account.

2. The current conclusion of the paper, and in particular the abstract, is based on the results for all-cause mortality. The results for CVD mortality are not entirely in agreement, and given that CVD-deaths are over 1/3 of the total number of deaths, the conclusions should take these results into account. Some further discussion of the possible other important causes of death for this age group would also be useful. Are the results, which indicate that BMI is not associated with these other causes of death for a given diet quality, reasonable?

3. Further discussion of the importance of the different body compartments that BMI collectively measures in this age group would be very helpful in interpreting the results, particularly in this context of a very long follow up. The brief discussion of sarcopenia is interesting, but how does this relate to the normal BMI category? Would these participants be considered sarcopenic and frail? What are the chances that some of the obese participants actually have a high muscle mass for their age, and that this is protective? What might be considered a healthy or an unhealthy BMI in this age group, and why? While the authors explicitly state that the study cannot tell us about changes in BMI or diet quality over time, the data are available to cast some light on this…

Minor comments:

1. Please be mindful of the terms used to describe the results. For example in a couple of places, the manuscript mentions presenting rates, but the results are hazard ratios (ie a relative measure of association).

2. Please consider including the DAG used to select confounders in the supplements. For instance, I am curious how diabetes can be a (time-varying?) confounder, when this disease is partially caused by elevated BMI and poor dietary habits. I would think that diabetes was an intermediate variable, and that adjusting for this would cause bias of the associations. I am also curious as to why height is considered a confounder.

3. The purpose of the analyses presented in figure 2 is not clearly described, and the results are not interpreted in depth. Please either justify these analyses more clearly, or omit them.

4. The test used to derive the p for homogeneity is not described in the methods section.

-----------------------------------------------------------

Reviewer #3: Michaëlsson et al evaluated the combined associations of BMI and adherence to a Mediterranean-like diet on all-cause and CVD mortality among men and women in the Swedish Mammography Cohort and the Cohort of Swedish Men. When analyses were cross-classified by categories of BMI and mMED score, the authors identified the lowest rates of all-cause mortality among overweight individuals with high mMED compared with those with normal weight and high mMED. At the same time, obese individuals with high mMED did not experience higher rates of all-cause mortality while individuals with a low mMED had a high mortality despite a normal BMI. This is a well-done, interesting, and important analysis highlighting the modifying effect of diet. However, there are major methodological considerations outlined below that need to be adequately addressed:

1. Throughout the manuscript, please avoid use of causal terminology (eg: reduced � replace with "lower") given the observational nature of the study. Along these lines, it is recommended that the authors replace the term "impact" in the title to a non-causal term such as "associations".

2. This study does not aim to compare the relative effectiveness of lowering BMI versus a higher diet quality. Therefore, the "conclusion" in the abstract and the "meaning" in the key points section should reflect what the study actually did - that diet quality "modifies" the higher risk seen with higher BMI.

3. Replace the term "subjects" with "participants".

4. Respiratory diseases (such as COPD and pulmonary disease) are the major causes of low BMI. Did the authors consider excluding these individuals?

5. Several studies have shown that confounding due to smoking is strikingly strong in analyses of BMI and mortality and that complete elimination of confounding is only possible by restricting to never smokers. The current analysis remains severely confounded by smoking status which may partly explain the higher HR's in the normal BMI range compared to the obese BMI range. The authors should attempt to carefully and completely control for confounding due to smoking status.

6. Given that pack-years of smoking was available, it is not clear why this was not adjusted for in the primary analysis. Residual confounding due to smoking is inevitable, it is critical to adjust for this as completely as possible.

7. For CVD mortality analysis, did the authors exclude those with CVD at baseline?

8. The "normal" range of BMI consists of a very heterogenous group of participants including chronic smokers (see higher % of current smokers in the normal BMI category in Table 1), those with preexisting conditions, and healthy individuals. Although excluding individuals with a BMI <20 kg/m2 excludes to some extent the reverse causation due to preexisting diseases, any analyses examining BMI and mortality should consider all the following exclusions/restrictions simultaneously - 1) restrict analyses to never smokers, 2) exclude those with preexisting disease (including respiratory disease and CVD and not just cancer), 3) and exclude deaths during the first few years of follow-up. While the authors attempted to do some of these in sensitivity analyses (data not shown), it is not clear if these were done concurrently.

9. It is recommended that the authors present analyses restricted to never smokers, normal BMI restricted to 22-25 kg/m2, excluding the first two years of follow-up and those with pre-existing diseases (respiratory diseases, cancer, CVD) as the primary analyses.

-----------------------------------------------------------

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Sep 17;17(9):e1003331. doi: 10.1371/journal.pmed.1003331.r003

Author response to Decision Letter 1

24 Jun 2020

Attachment

Submitted filename: Response PMEDICINE-D-20-00848R1_200623.docx

Click here for additional data file.^{(605.7KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003331.r004

Decision Letter 2

Thomas J McBride

30 Jul 2020

Dear Dr. Michaëlsson,

Thank you very much for re-submitting your manuscript "Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: a cohort study in women and men" (PMEDICINE-D-20-00848R2) for consideration at PLOS Medicine.

I have discussed the paper with editorial colleagues and it was also seen again by two reviewers. As a result I am pleased to tell you that, provided the remaining editorial and production issues are fully dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please let me know if you have any questions. Otherwise, we look forward to receiving the revised manuscript shortly.

Sincerely,

Richard Turner PhD, for Thomas McBride, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

------------------------------------------------------------

Requests from Editors:

1- From the Financial Disclosure: “We acknowledge the national research infrastructure SIMPLER for provisioning of facilities and experimental support and we would like to thank Anna-Karin Kolseth for her

assistance.” should be placed in the Acknowledgements section at the end of the main text.

2- Please update your data statement to read: “Data cannot be shared publicly because of the sensitive nature of the data and the GDPR legislation. Data *are* available from the national research infrastructure SIMPLER for researchers who meet the criteria for access to confidential data. Details of how to obtain data from the national research infrastructure SIMPLER can be obtained at the website www.simpler4health.se” And add additional information that researchers will need to identify this specific dataset (e.g., doi or accession number).

3- Please update the title to “Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: a cohort study”

4- Please include the adjustment factors in the Abstract Methods and Findings section.

5- In the Abstract Methods and Findings, please rephrase the reporting of the main outcomes to make it clear that reference for all comparisons is the normal weight and high mMED group.

6- Thank you for adding the study limitations to the Abstract. Please provide limitations that are a bit more specific to this particular study (e.g., self report, some potential sources of unmeasured confounding).

7- "... suggest that, for older women and men, ..." at the end of the abstract (note misplaced comma).

8- Thank you for including an Author Summary. Please reformat to bullet points, 1-2 sentences per bullet point, up to 3 bullets per section.

9- Thank you for including your study protocol in the supplemental files. Please also include an english translation. Additionally, the analysis plan posted at protocols.io is not publicly available, please make it so.

10- Thank you for including your STROBE statement. Please replace the page numbers with paragraph numbers per section (e.g. "Methods, paragraph 1"), since the page numbers of the final published paper may be different from the page numbers in the current manuscript.

12- I assume the questionnaires included informed consent, but please state so explicitly when describing the cohorts in the Methods section.

13- Please edit the Figure 1 legend to make clear which graphs are all-cause or CVD mortality. It would also be helpful to include labels in the figure itself. Please also describe what the shaded regions and the histogram along the bottom represent.

14- Similarly, in the Figure 2 legend, please describe what the whiskers represent (95%CIs, I presume).

15- Please include some discussion of the implications and next steps for research, clinical practice, and/or public policy just before the concluding paragraph.

16- The Article Information section should be removed from the end of the main text, and the information should appear in the relevant metadata sections via the submission form.

17- Reference 12 and some others contain competing interest information that can be cut.

Comments from Reviewers:

*** Reviewer #1:

Thank you to the authors for addressing my previous comments well. I have no further issues to raise.

*** Reviewer #2:

The authors have thoughtfully and thoroughly addressed my comments and concerns. The use of several methods to estimate risk differences, which all corroborate their initial findings, strengthen the manuscript (although I would have enjoyed more discussion of these findings in the Discussion section).

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1003331.r005

Decision Letter 3

Thomas J McBride

14 Aug 2020

Dear Professor Michaëlsson,

On behalf of my colleagues and the academic editor, Dr. Christina Catherine Dahm, I am delighted to inform you that your manuscript entitled "Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: a cohort study" (PMEDICINE-D-20-00848R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 STROBE Checklist. Checklist according to STROBE guidelines.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.^{(30.4KB, docx)}

S1 Protocol. Prospective study plan.

(DOCX)

Click here for additional data file.^{(26.7KB, docx)}

S2 Protocol. Directed acyclic graph with code displaying the selection of covariates for the analysis of association of BMI combined with adherence to a Mediterranean-like diet with mortality.

BMI, body mass index

(DOCX)

Click here for additional data file.^{(945.9KB, docx)}

S1 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after exclusion of those with BMI higher than 35 kg/m².

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(149.8KB, docx)}

S2 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after restriction of the analysis to those with normal BMI to a more narrow 22–25 kg/m² range.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(141.9KB, docx)}

S3 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after extending the multivariable model by additional adjustment for pack-years of smoking.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(149.8KB, docx)}

S4 Fig. Associations of combinations of BMI and adherence to an mMED with all-cause mortality after restriction to never-smokers.

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(144.1KB, docx)}

BMI, body mass index; mMED, modified Mediterranean-like diet

(DOCX)

Click here for additional data file.^{(146.7KB, docx)}

S1 Table. Associations of combinations of BMI and adherence to an mMED with all-cause mortality.

(DOCX)

Click here for additional data file.^{(15.3KB, docx)}

Attachment

Submitted filename: Response PMEDICINE-D-20-00848R1_200623.docx

Click here for additional data file.^{(605.7KB, docx)}

Data Availability Statement

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PERMALINK

Combined associations of body mass index and adherence to a Mediterranean-like diet with all-cause and cardiovascular mortality: A cohort study

Karl Michaëlsson

John A Baron

Liisa Byberg

Jonas Höijer

Susanna C Larsson

Bodil Svennblad

Håkan Melhus

Alicja Wolk

Eva Warensjö Lemming

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Methods

BMI

Modified Mediterranean-like diet (mMED) score

Assessment of covariates

Assessment of deaths

Statistical analysis

Results

Table 1. Age-standardized baseline characteristics of the participants by 3 categories of BMI and 3 categories of Mediterranean diet score, respectively.

Fig 1. Association between BMI (A for all-cause mortality and B for cardiovascular mortality) and an mMED score (C for all-cause mortality and D for cardiovascular mortality) with mortality.

Fig 2. Subgroup analysis for BMI as continuous variable below or above the median of 26 kg/m2 (A) and for mMED (B) as a continuous variable by categories of the covariates.

Fig 3. Associations of combinations of BMI and adherence to an mMED with all-cause (A) and CVD mortality (B).

Table 2. Combined associations of a Mediterranean diet score and BMI on all-cause mortality in women and in men.

Discussion

Potential mechanisms

Strengths and limitations

Conclusions

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Louise Gaynor-Brook

Roles

Decision Letter 1

Emma Veitch

Roles

Author response to Decision Letter 1

Decision Letter 2

Thomas J McBride

Roles

Decision Letter 3

Thomas J McBride

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Links to NCBI Databases

Fig 2. Subgroup analysis for BMI as continuous variable below or above the median of 26 kg/m² (A) and for mMED (B) as a continuous variable by categories of the covariates.