Skip to main content
Download PDF

Association of Lifelines Diet Score (LLDS) and metabolically unhealthy overweight/obesity phenotypes in women: a cross-sectional study

BMC Women's Health volume 22, Article number: 374 (2022) Cite this article

Abstract

Background

Previous studies have shown the association of a number of dietary quality scores with metabolically phenotypes of obesity. Recently, the Lifelines Diet Score (LLDS), which is a fully food-based score based on the 2015 Dutch dietary guidelines and underlying international literature, has been proposed as a tool for assessing the quality of the diet. Therefore, this study was performed to investigate the association between LLDS and metabolically healthy/unhealthy overweight and obesity (MHO/MUHO) phenotypes.

Methods

This study was performed on 217 women, aged 18–48 years old. For each participant anthropometric values, biochemical test and body composition were evaluated by standard protocols and methods. The LLDS was determined based on 12 components using a valid and reliable food frequency questionnaire (FFQ) containing 147 items. The metabolically healthy (MH) was evaluated using the Karelis criteria.

Results

Among the total participants in this study, 31.3% of the subjects were MHO while 68.7% were MUHO. After adjustment for potential confounding variables (age, energy intake, and physical activity), participants in highest LLDS tertile had a lower odds of MUHO compared with those in the lowest tertile (OR: 1.18; 95% CI: 0.23, 5.83; P-trend = 0.03). Also, after further adjustment with BMI, provided only small changes in "OR" and did not attenuate the significance (OR: 1.28; 95% CI: 0.23, 6.91; P-trend = 0.02).

Conclusions

The present evidence indicates that individuals with higher adherence to the LLDS had lower odds of metabolically unhealthy (MUH).

Peer Review reports

Introduction

Obesity, as one of the most important metabolic diseases with phenotypic diversity, has a growing prevalence in all age groups around the world [1,2,3]. A subset of obesity that has recently attracted a great deal of attention is the metabolically healthy overweight/obesity (MHO), in which individuals, despite having excessive body fatness, show a favorable metabolic profile compared to their counterparts with similar body mass index (BMI) [4,5,6,7]. People with the MHO phenotype are characterized by high levels of insulin sensitivity, low levels of inflammatory markers, greater cardiorespiratory readiness, lower visceral fat ratio, lower liver fat, the absence of any dyslipidemia or hyperlipidemia, and lower prevalence of hypertension [8, 9]. It is estimated that the prevalence of this phenotype is about one third of obese people [4]. Evidence suggests that MHO is a temporary condition and individuals do not remain in this phenotype permanently [10, 11]. One study found that over a period of 5–10 years, nearly 30% of people with the MHO phenotype changed to an unhealthy metabolic status [12]. Also, in another study, in a 6-year follow-up, nearly 30% to 40% of people with the MHO phenotype changed to another phenotype, metabolically unhealthy overweight/obesity (MUHO) [13]. Unlike MHO people, MUHOs show an increased risk of type 2 diabetes (T2D), cardiovascular disease (CVD), and various cancers and are also associated with obesity-related metabolic disorders such as insulin resistance (IR) and dyslipidemia [14]. Factors such as age, sex, genetics and lifestyle factors may be associated with the transition from MHO to MUHO [15,16,17,18].

The importance of nutrition in the development of non-communicable diseases is well known. Studies have shown that the quality of diet as one of the determinants of obesity [19], when low, is associated with diseases such as diabetes mellitus (DM) and metabolic syndrome (MetS) [20, 21]. Numerous dietary scores have been developed around the world to measure adherence to dietary guidelines and dietary patterns [22,23,24]. The Lifelines Diet Score (LLDS) has been proposed as a measure of the relative quality of the diet [25]. This score was calculated based on the Dutch dietary guidelines for 2015, which are based entirely on scientific evidence and detailed studies on the relationship between foods and dietary patterns with chronic diseases [25, 26]. This dietary score consists of nine food groups that have positive effects on health, including vegetables, fruits, whole grain products, legumes and nuts, fish, oils and soft margarines, unsweetened dairy, coffee, and tea, and three other food groups of red and processed meat, butter and hard margarines, and sugar-sweetened beverages that have negative effects on health [25, 26]. Studies have shown that intakes a healthy diet including fruits, vegetables, whole grains and fish is inversely associated with diabetes, CVD and MetS [27, 28], and intakes processed meats and sugary drinks is associated with an increased risk [29, 30]. On the other hand, receiving a healthier diet has been shown to be positively associated with the MHO phenotype [31]. Therefore, in view of the above and the fact that to date no study has examined the association between LLDS and metabolic phenotypes of obesity, this study was conducted with the aim of whether LLDS increases healthy metabolic phenotypes.

Methods and materials

A group of 217 women aged 18–48 years participated in current study. The target population was randomly selected from obese women who were referred to health centers affiliated to Tehran University of Medical Sciences (TUMS). All participants signed a written consent form. The medical research ethics committee of TUMS approved the study with the following identification. All anthropometric assessments and measurements were obtained by an experienced nutritionist. Participants had a BMI ≥ 25 kg/m2 to enter the study. Exclusion criteria include any acute or chronic illness, including cardiovascular disease, diabetes, cancer, thyroid disease, kidney disease, as well as people who are currently pregnant, breastfeeding, menopause, consuming alcohol and smoking, special diet and regular use of any medications or supplements. Also, people with energy intake < 800 kcal/d and 4200 kcal/d < were excluded from the study.

Measurement of biochemical parameters

A complete description of the measurement method is given in the previous study [9]. Following an overnight fasting, fasting blood sugar (FBS), triglyceride (TG), total cholesterol, high-density lipoprotein (HDL), low density lipoprotein (LDL), and high-sensitive C-reactive protein (hs-CRP), serum insulin concentrations, were measured [9].

Body composition and anthropometric measurements assessment

Body composition was assessed using the InBody 770 scanner. The protocol of which has been described in other studies [9]. Weight was measured using calibrated digital scales and height was measured by a wall-mounted stadiometer. Also, waist circumference (WC) from midpoint between the last rib and the iliac crest, and hip circumference (HC) the most prominent part is marked and without imposing any pressure on the body with a precision of 0.5 cm were calculated. BMI was also calculated as weight (kg) divided by height (m2).

Assessment of blood pressure

To measure blood pressure, participants rested for 10–15 min, then blood pressure was measured using sphygmomanometer (Omron, Germany, European).

The HOMA-IR calculation

The insulin resistance homeostatic model assessment (HOMA-IR) according to the following equation: [fasting plasma glucose (mmol/l) × fasting plasma insulin (mIU/l)]/22.5 [32].

Definition of metabolic health and its components

We used karelis criteria according to which the presence of 4 or more of the following items indicates a healthy phenotype: TG ≤ 1.7 mmol/l, HDL ≥ 1.3 mmol/l and no treatment, LDL ≤ 2.6 mmol/l and no treatment, hs-CRP ≤ 3.0 mg/l, and HOMA-IR ≤ 2.7 [33].

Assessment of dietary intake

A semi-quantitative food frequency questionnaire (FFQ) with 147 food items including a list of foods consumed during the past year was used to assess dietary intake. All participants were asked about the amount and frequency of each food item consumed daily, weekly or monthly. The reported frequency for each food item was then converted to grams/day. Evaluation of dietary nutrient intake was performed using N4 (First Data Bank, San Bruno, CA) software with its database adapted for Iranian foods. This questionnaire also had high validity and reliability [34].

Lifelines Diet Score

The LLDS is a tool for ranking people about the relative quality of the diet, calculated using the method of Vinke et al. [25]. This dietary privilege is explained in detail elsewhere [25, 35]. In summary, according to the Dutch diet guidelines in 2015, which are completely based on scientific evidence, LLDS includes the consumption of nine food groups of vegetables, fruit, whole grain products, legumes and nuts, fish, oils and soft margarines, unsweetened dairy, coffee, and tea that have been proven to have positive effects on health, and three food groups including red and processed meat, butter and hard margarines, and sugar-sweetened beverages that have a negative effect on health. Individual’s food intake was expressed in grams per 1000 kcal. For each food group, intake was divided into 1 to 5 quintiles, that 5 points are given to the maximum intake and 1 point is given to the minimum intake of positive food groups, and for negative food groups, 5 points are awarded to the lowest intake and 1 point is given to the minimum intak. Finally, the sum of the scores of the 12 components is considered as LLDS score, which is in the range of 12–60 [25, 35].

Assessment of other variables

To determine the level of physical activity of the participants, we used a validated international physical activity questionnaire (IPAQ). According to the IPAQ scoring protocol, individuals were divided into the following groups in terms of physical activity: (1) low active (< 600 MET-h/week); (2) moderate active (≥ 600 MET-h/week); and (3) high active (≥ 3000 MET-h/week) [36]. We also used a standard socio-demographic questionnaire to collect data age, education level, marital status, job, supplementation, and economic status.

Statistical analysis

To evaluate the normal distribution of variables, the Kolmogorov–Smirnov test was used, which was normal. Data on continuous characteristics were reported as the mean ± standard deviations (SDs) and data on categorical characteristics were expressed as percentages and numbers. Chi-square test was used to evaluate significant differences of categorical variables among tertiles of LLDS score and one-way analysis of variance (ANOVA) was used to evaluate significant mean differences of continuous variables across tertiles of LLDS cutoff points (T1: ≤ 34, T2: 35–38, T3 ≥ 39). The post-hoc multiple comparison analysis by bonferroni has shown a significant mean difference between the groups. The analysis of covariance (ANCOVA) was used to identify dietary intakes and general characteristics mean differences between tertiles of the LLDS after adjusted by energy intake for the dietary intakes and further with age, physical activity, and BMI for general characteristics. We used binary logistic regressions to assess the association between LLDS score with obesity phenotypes and estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the presence of MUHO with LLDS score in crude and multivariable-adjusted models. Age, energy intake, and physical activity were controlled for in the first model. Further adjustment was made for BMI in the second model. The first tertile of LLDS score and MHO were considered as the reference categories. In the present study P values < 0.05 and P values = 0.05, 0.06 and 0.07 were considered as significant levels and marginally significant, respectively. All statistical analyzes via statistical package for social sciences (version 24; SPSS Inc., Chicago, IL, USA) was performed.

Result

Study population characteristics

A total of 217 obese women, 31.3% of the subjects were classified as MHO and 68.7% were MUHO, according to Karelis criteria. At this study population the mean and standard deviation (SD) age, height, weight, and BMI of participants were 36.40 (8.40) year, 161.41 (5.85) cm, 80.30 (11.33) kg, and 30.80 (3.82) kg/m2 respectively. Moreover, the mean (SD) indicator of Karelis criterion including TG, HDL, LDL, hs-CRP, and HOMA-IR of participants were 1.32 (0.76), 1.14 (0.24), 2.39 (0.62), 4.26 (4.57), and 3.36 (1.33), respectively. Among the participants, 175 (76.8%) women were married and 95 (41.7%) had a moderate economic status.

General characteristics of study population among tertiles of the LLDS score

The baseline characteristics of study participants, categorized according to the LLDS score, were presented in Table 1. As shown in this table, in the crude model, a significant mean difference was observed among tertiles of the LLDS score in terms of FFM (P = 0.04) and marginally significant for age (P = 0.06). After controlling for potentially confounding variables (age, energy intake, physical activity, BMI), the marginally significant was observed for hs-CRP (P = 0.06), which according to bonferroni post-hoc testing, the mean difference in hs-CRP was between T1 and T3, such that the mean was higher in T3. Also, after adjustment with energy intake, physical activity, BMI, there was a significant mean difference for the age of participants among tertiles of the LLDS score (P = 0.03), this mean difference was between T1 and T3 according to bonferroni post-hoc testing, so that age mean was more in T3 than T1. BMI was considered as collinear for anthropometrics and body composition variables. There were no significant mean differences in other variables among tertiles of LLDS scores (P > 0.05).

Table 1 General characteristics of study population among tertiles of the LLDS score in obese and overweight women (n = 217)
Full size table

Dietary intake of study subjects across tertiles of the LLDS score

Dietary intakes of participants across tertiles of LLDS score were presented in Table 2. After adjustment with the energy intake, there have shown the participants in the highest tertile of LLDS score had significantly higher intakes of vegetables, legumes, and nuts, fish, unsweetened dairy (P < 0.001), fruits (P = 0.001), oils, and soft margarine (P = 0.009), coffee (P = 0.004), but significantly lower intakes of red and processed meat (P = 0.01), butter and hard margarine (P < 0.001), sugar-sweetened beverages (P = 0.005). Also, after adjusting for energy intake there was a significant mean difference in dietary intakes of macronutrients and micronutrients, which indicated that the group with the highest adherence with LLDS had significantly higher intakes of protein, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), iron, magnesium, potassium, manganese, beta carotene, pantothenic acid, vitamin B6, biotin, folate (P < 0.001), total fiber, vitamin A, vitamin k, selenium (P = 0.01), zinc (P = 0.002), copper (P = 0.004), chromium (P = 0.04), vitamin C (P = 0.003), vitamin D, caffeine (P = 0.02), thiamin, riboflavin (P = 0.007), carbohydrates (P = 0.001), whereas had significantly lower intakes of total fat, saturated fatty acid (SFA), calcium (P < 0.001), monounsaturated fatty acid (MUFA), (P = 0.005), linolenic acid (P = 0.03).

Table 2 Dietary intakes of study subjects according to tertiles of the LLDS score in obese and overweight women (n = 217)
Full size table

The association between LLDS score with obesity phenotypes

The association between LLDS score with obesity phenotypes and the OR and 95% CIs for MUHO across tertile categories of LLDS score compare to MHO as reference group is indicated in Table 3. There was no significant association between obesity phenotypes with LLDS score in the crude model (OR: 0.97; 95% CI: 0.24, 3.89; P = 0.97), even after adjustment for potential confounding variables including age, energy intake, and physical activity in model 1 (OR: 1.18; 95% CI: 0.23, 5.83; P = 0.83) and also an additional adjustment for BMI in model 2 (OR: 1.28; 95% CI: 0.23, 6.91; P = 0.77), no association was observed. However, the association between LLDS score and obesity phenotypes had statistically significant trends across LLDS tertiles. In the crude model, the odds of MUHO compared to MHO had higher with more adherence of LLDS, and there was not statistically significant trend (P-trend = 0.58), but after adjustment for potential confounding variables (age, energy intake, and physical activity) in model 1, participants in highest LLDS tertile had a lower odds of MUHO compared with those in the lowest tertile (OR: 1.18; 95% CI: 0.23, 5.83; P-trend = 0.03). Model 2, with further adjustment with BMI, provided only small changes in "OR" and did not attenuate the significance (OR: 1.28; 95% CI: 0.23, 6.91; P-trend = 0.02).

Table 3 Crude and multivariable-adjusted odds ratios and 95% CIs for obesity phenotypes (MHO in comparison to MUO) across tertiles of LLDS score in obese and overweight women (n = 217)
Full size table

Discussion

In the present study, we investigated the association between LLDS and healthy metabolic phenotype of obesity. This is the first study that investigats the association between LLDS and healthy metabolic phenotype of obesity. In this study, no association was found between LLDS and MH, but with increasing LLDS adherence, significant trends in MH odds were observed.

Our results showed that with increasing tertiles, the average age of individuals and positive food groups consumption, increased, and negative food groups consumption were decreased also the amount of hs-CRP decreased with increasing LLDS tertile. As mentioned in the introduction, the LLDS has been proposed as a measure of the relative quality of the diet based on the Dutch dietary guidelines [25], which with increasing LLDS adherence, the quality of the diet has increased. A study on women with MH and MUH found that hs-CRP levels decreased with increasing healthy eating pattern's tertile. Many studies have shown that in obese people, the level of inflammatory factors secreted by adipose tissue such as IL-6 and TNF-alpha increase, which stimulates the production and secretion of acute phase proteins in the liver [37]. Elevated plasma levels of these inflammatory factors and hs-CRP are associated with an increased risk of IR and CVD [38]. In healthy dietary pattern, intake of foods including vegetables, fruits are reported to be highly correlated with inflammatory markers, especially hs-CRP [9]. It has also been shown that a diet rich in vegetables, fruits, and low in total fat and saturated fats may reduce inflammatory markers [9]. The high content of vitamin C and fiber in fruits and vegetables may reduce hs-CRP [39]. In addition to the effect of fruits and vegetables on this inflammatory factor, intake of olives [40] and fish [41] is also effective in this healthy dietary pattern. Studies have shown that fish and seafoods are inversely associated with hs-CRP levels due to the presence of long-chain n-3 PUFAs [42].

A diet rich in vegetables, nuts, and fruits is associated with reducing inflammation due to the high contents of magnesium, fiber, and antioxidants [43]. As in the present study, with increasing LLDS adherence the level of hs-CRP decreased. Also, with increasing LLDS adherence, the amount of magnesium (Mg), potassium (K), and vitamin C has increased. High levels of Mg, phytochemicals, K, and vitamin C intake are associated with decreased IR, which is a factor in the development of MUH [44]. Previous studies have shown that high fruit and vegetable intake are associated with a lower risk of metabolic syndrome, CVD [28], and also stated that fruits intake is associated with a lower risk of type 2 diabetes [45, 46]. In the present study, more LLDS adherence is associated with higher calcium (Ca), Mg, total fiber, and lower total fat intake, which decreased hs-CRP levels [47, 48]. Consumption of butter and hard margarines, red and processed meats also decreased with the increaseing tertiles, as reported in previous data an unhealthy diet is associated with a higher chance of MUH, which includes consuming more solid fats, red meat, brain, liver and kidney are known [9]. The Dutch lifelines cohort study done by Slagter et al. on dietary patterns in people aged 30–69 years, found that in the upper quartile of a healthier diet, rich in vegetables, fruits, and fish, unsweetened fermented milk products, and the prohibition of sweet beverages and sweet snacks; have a higher chance of MH in women [31]. As in our study with increasing LLDS adherence significant trends in MH odds were observed. Epidemiological and experimental studies have also shown that dairy intake, has beneficial effects on metabolic syndrome risk factors, and associated with reducing risk of obesity, CVD and body fat gain [49]. As in our study, consumption of unsweetened fermented dairy products increased, and consumption of sugar-sweetened beverages decreased with increasing tertiles.

While carbohydrate intake in participants increased by increasing tertiles, fiber intake has also increased. In the Dutch cohort study, it was observed that the consumption of bread, potatoes and sweet snacks was inversely related to MH, which is due to the high content of carbohydrates, especially refined carbohydrates, and high glycemic index, and low fiber content [50]. Also, high glycemic index and low fiber content are associated with IR and impaired glucose tolerance, followed by metabolic syndrome [51], while increasing carbohydrates intakes was associated with increased fiber intake, which may be associated with an increased odds of MH. In general, studies and data showed that receiving a healthy diet that is rich in vitamins, minerals, phytochemicals and etc. is effective in a healthy metabolic obesity.

The strengths of this study are the use of the FFQ questionnaire validated in the Iranian population. Examining this association in one gender and only in Tehran is one of the weaknesses of this study because these results can not be attributed to the entire Iranian society and all individuals.

Conclusion

The LLDS did not have a significant association with MH, but it was found that further adherence to the LLDS could have significant trends with odds of MH. So, it is recommended to increase adherence to LLDS and healthy dietary pattern in obese people to increase the odds of MH. More longitudinal studies are needed to confirm these findings through replication in more diverse populations and at last to confirm this correlation.

Availability of data and materials

The data that support the findings of this study are available from Khadijeh Mirzaei but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Khadijeh Mirzaei.

Abbreviations

BF%:

Body fat percentage

BFM:

Body fat mass

BMI:

Body mass index

CIs:

Confidence intervals

CVD:

Cardiovascular diseases

DBP:

Diastolic blood pressure

DHA:

Docosahexaenoic Acid

EPA:

Eicosapentaenoic Acid

FBS:

Fasting blood sugar

FFM:

Fat free mass

FFMI:

Fat free mass index

FMI:

Fat mass index

HC:

Hip circumferences

HDL-C:

High density lipoprotein cholesterol

hs-CRP:

High-sensitivity C-reactive protein

IR:

Insulin resistance

IPAQ:

International physical activity questionnaire

LDL-C:

Low density lipoprotein cholesterol

LLDS:

Lifelines Diet Score

MH:

Metabolically healthy

MHO:

Metabolically healthy overweight/obesity

MUH:

Metabolically unhealthy

MUHO:

Metabolically unhealthy overweight/obesity

PA:

Physical activity

SBP:

Systolic blood pressure

SDs:

Standard deviations

SFA:

Saturated fatty acid

T:

Tertile

T2D:

Type 2 diabetes

TBW:

Total body water

TC:

Total cholesterol

TG:

Triglyceride

WC:

Waist circumference

WHR:

Waist-hip ratio

References

  1. Campos P, Saguy A, Ernsberger P, Oliver E, Gaesser G. The epidemiology of overweight and obesity: public health crisis or moral panic? Int J Epidemiol. 2006;35(1):55–60.

    Article  PubMed  Google Scholar 

  2. Wang Y, Mi J, Shan XY, Wang QJ, Ge KY. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obes. 2007;31(1):177–88.

    Article  CAS  Google Scholar 

  3. Zhang Y, Liu J, Yao J, Ji G, Qian L, Wang J, et al. Obesity: pathophysiology and intervention. Nutrients. 2014;6(11):5153–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lin H, Zhang L, Zheng R, Zheng Y. The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: a systematic review and meta-analysis: a PRISMA-compliant article. Medicine (Baltimore). 2017;96(47):e8838.

    Article  Google Scholar 

  5. Hwang L-C, Bai C-H, Sun C-A, Chen C-J. Prevalence of metabolically healthy obesity and its impacts on incidences of hypertension, diabetes and the metabolic syndrome in Taiwan. Asia Pac J Clin Nutr. 2012;21(2):227–33.

    CAS  PubMed  Google Scholar 

  6. Alam I, Ng TP, Larbi A. Does inflammation determine whether obesity is metabolically healthy or unhealthy? The aging perspective. Mediators Inflamm. 2012;2012:1–14.

    Article  Google Scholar 

  7. Muñoz-Garach A, Cornejo-Pareja I, Tinahones FJ. Does metabolically healthy obesity exist? Nutrients. 2016;8:320.

    Article  PubMed Central  CAS  Google Scholar 

  8. Blüher M. Metabolically healthy obesity. Endocr Rev. 2020;41(3):405–20.

    Article  Google Scholar 

  9. Mirzababaei A, Sajjadi SF, Ghodoosi N, Pooyan S, Arghavani H, Yekaninejad MS, et al. Relations of major dietary patterns and metabolically unhealthy overweight/obesity phenotypes among Iranian women. Diabetes Metab Syndr Clin Res Rev. 2019;13(1):322–31.

    Article  Google Scholar 

  10. Zheng R, Liu C, Wang C, Zhou B, Liu Y, Pan F, et al. Natural course of metabolically healthy overweight/obese subjects and the impact of weight change. Nutrients. 2016;8(7):430.

    Article  PubMed Central  CAS  Google Scholar 

  11. Kramer CK, Zinman B, Retnakaran R. Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann Intern Med. 2013;159:758–69.

    Article  PubMed  Google Scholar 

  12. Appleton SL, Seaborn CJ, Visvanathan R, Hill CL, Gill TK, Taylor AW, et al. Diabetes and cardiovascular disease outcomes in the metabolically healthy obese phenotype: a cohort study. Diabetes Care. 2013;36(8):2388–94.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Soriguer F, Gutiérrez-Repiso C, Rubio-Martín E, García-Fuentes E, Almaraz MC, Colomo N, et al. Metabolically healthy but obese, a matter of time? Findings from the prospective Pizarra study. J Clin Endocrinol Metab. 2013;98(6):2318–25.

    Article  CAS  PubMed  Google Scholar 

  14. Shaharyar S, Roberson LL, Jamal O, Younus A, Blaha MJ, Ali SS, et al. Obesity and metabolic phenotypes (metabolically healthy and unhealthy variants) are significantly associated with prevalence of elevated C-reactive protein and hepatic steatosis in a large healthy Brazilian population. J Obes. 2015;2015:1–6.

    Article  Google Scholar 

  15. Eshtiaghi R, Keihani S, Hosseinpanah F, Barzin M, Azizi F. Natural course of metabolically healthy abdominal obese adults after 10 years of follow-up: the Tehran Lipid and Glucose Study. Int J Obes. 2015;39(3):514–9.

    Article  CAS  Google Scholar 

  16. Achilike I, Hazuda HP, Fowler SP, Aung K, Lorenzo C. Predicting the development of the metabolically healthy obese phenotype. Int J Obes. 2015;39(2):228–34.

    Article  CAS  Google Scholar 

  17. Hamer M, Bell JA, Sabia S, Batty GD, Kivimäki M. Stability of metabolically healthy obesity over 8 years: the English Longitudinal Study of Ageing. Eur J Endocrinol. 2015;173(5):703–8.

    Article  CAS  PubMed  Google Scholar 

  18. Van Vliet-Ostaptchouk JV, Nuotio M-L, Slagter SN, Doiron D, Fischer K, Foco L, et al. The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies. BMC Endocr Disord. 2014;14(1):1–13.

    Google Scholar 

  19. Karimbeiki R, Safavi M, Feizi A, Abbasi B, Nikpour M. The relationship between dietary diversity score and obesity in Tehranian adults: a case-control study. Iran J Endocrinol Metab. 2016;18(4):260–9.

    Google Scholar 

  20. Choi J-K, Kim M-Y, Kim J-K, Park J-K, Oh S-S, Koh S-B, et al. Association between short sleep duration and high incidence of metabolic syndrome in midlife women. Tohoku J Exp Med. 2011;225(3):187–93.

    Article  PubMed  Google Scholar 

  21. Ayas NT, White DP, Manson JE, Stampfer MJ, Speizer FE, Malhotra A, et al. A prospective study of sleep duration and coronary heart disease in women. Arch Intern Med. 2003;163(2):205–9.

    Article  PubMed  Google Scholar 

  22. Sofi F, Macchi C, Abbate R, Gensini GF, Casini A. Mediterranean diet and health status: an updated meta-analysis and a proposal for a literature-based adherence score. Public Health Nutr. 2014;17(12):2769–82.

    Article  PubMed  Google Scholar 

  23. Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HAB, Kuczynski KJ, et al. Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet. 2013;113(4):569–80.

    Article  PubMed  Google Scholar 

  24. Kennedy ET, Ohls J, Carlson S, Fleming K. The healthy eating index: design and applications. J Am Diet Assoc. 1995;95(10):1103–8.

    Article  CAS  PubMed  Google Scholar 

  25. Vinke PC, Corpeleijn E, Dekker LH, Jacobs DR, Navis G, Kromhout D. Development of the food-based Lifelines Diet Score (LLDS) and its application in 129,369 Lifelines participants. Eur J Clin Nutr. 2018;72(8):1111–9.

    Article  PubMed  Google Scholar 

  26. Kromhout D, Spaaij CJK, de Goede J, Weggemans RM. The 2015 Dutch food-based dietary guidelines. Eur J Clin Nutr. 2016;70(8):869–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Esmaillzadeh A, Mirmiran P, Azizi F. Whole-grain consumption and the metabolic syndrome: a favorable association in Tehranian adults. Eur J Clin Nutr. 2005;59(3):353–62.

    Article  CAS  PubMed  Google Scholar 

  28. Esmaillzadeh A, Kimiagar M, Mehrabi Y, Azadbakht L, Hu FB, Willett WC. Fruit and vegetable intakes, C-reactive protein, and the metabolic syndrome. Am J Clin Nutr. 2006;84(6):1489–97.

    Article  CAS  PubMed  Google Scholar 

  29. Feskens EJM, Sluik D, van Woudenbergh GJ. Meat consumption, diabetes, and its complications. Curr Diab Rep. 2013;13(2):298–306.

    Article  CAS  PubMed  Google Scholar 

  30. Greenwood DC, Threapleton DE, Evans CEL, Cleghorn CL, Nykjaer C, Woodhead C, et al. Association between sugar-sweetened and artificially sweetened soft drinks and type 2 diabetes: systematic review and dose–response meta-analysis of prospective studies. Br J Nutr. 2014;112(5):725–34.

    Article  CAS  PubMed  Google Scholar 

  31. Slagter SN, Corpeleijn E, Van Der Klauw MM, Sijtsma A, Swart-Busscher LG, Perenboom CWM, et al. Dietary patterns and physical activity in the metabolically (un) healthy obese: the Dutch Lifelines cohort study. Nutr J. 2018;17(1):1–14.

    Article  Google Scholar 

  32. Mirzaei K, Hossein-Nezhad A, Keshavarz SA, Eshaghi SM, Koohdani F, Saboor-Yaraghi AA, et al. Insulin resistance via modification of PGC1α function identifying a possible preventive role of vitamin D analogues in chronic inflammatory state of obesity: a double blind clinical trial study. Minerva Med. 2014;105(1):63–78.

    CAS  PubMed  Google Scholar 

  33. Hinnouho GM, Czernichow S, Dugravot A, Batty GD, Kivimaki M, Singh-Manoux A. Metabolically healthy obesity and risk of mortality: does the definition of metabolic health matter? Diabetes Care. 2013;36(8):2294–300.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Mirmiran P, Hosseini Esfahani F, Mehrabi Y, Hedayati M, Azizi F. Reliability and relative validity of an FFQ for nutrients in the Tehran Lipid and Glucose Study. Public Health Nutr. 2010;13(5):654–62.

    Article  PubMed  Google Scholar 

  35. Khani-Juyabad S, Setayesh L, Tangestani H, Ghodoosi N, Sajjadi SF, Badrooj N, et al. Adherence to Lifelines Diet Score (LLDS) is associated with better sleep quality in overweight and obese women. Eat Weight Disord Anorexia Bulim Obes. 2021;26(5):1639–46.

    Article  Google Scholar 

  36. Fan M, Lyu J, He P. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ). 2005. Zhonghua Liu Xing Bing Xue Za Zhi. 2014;35:961–4. http://www.IPAQ.ki.se

  37. Yudkin JS, Stehouwer CDA, Emeis JJ, Coppack SW. Obesity, insulin resistance, and endothelial dysfunction. Arterioscler Thromb Vasc Biol. 1999;19(4):972–8.

    Article  CAS  PubMed  Google Scholar 

  38. Blankenberg S, Barbaux S, Tiret L. Adhesion molecules and atherosclerosis. Atherosclerosis. 2003;170(2):191–203.

    Article  CAS  PubMed  Google Scholar 

  39. Lowe G, Woodward M, Rumley A, Morrison C, Tunstall-Pedoe H, Stephen K. Total tooth loss and prevalent cardiovascular disease in men and women: possible roles of citrus fruit consumption, vitamin C, and inflammatory and thrombotic variables. J Clin Epidemiol. 2003;56(7):694–700.

    Article  PubMed  Google Scholar 

  40. Schwingshackl L, Christoph M, Hoffmann G. Effects of olive oil on markers of inflammation and endothelial function—a systematic review and meta-analysis. Nutrients. 2015;7(9):7651–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ghasemi Fard S, Wang F, Sinclair AJ, Elliott G, Turchini GM. How does high DHA fish oil affect health? A systematic review of evidence. Crit Rev Food Sci Nutr. 2019;59(11):1684–727.

    Article  CAS  PubMed  Google Scholar 

  42. Micallef MA, Munro IA, Garg ML. An inverse relationship between plasma n-3 fatty acids and C-reactive protein in healthy individuals. Eur J Clin Nutr. 2009;63(9):1154–6.

    Article  CAS  PubMed  Google Scholar 

  43. Saneei P, Hashemipour M, Kelishadi R, Esmaillzadeh A. The dietary approaches to stop hypertension (DASH) diet affects inflammation in childhood metabolic syndrome: a randomized cross-over clinical trial. Ann Nutr Metab. 2014;64(1):20–7.

    Article  CAS  PubMed  Google Scholar 

  44. Most MM. Estimated phytochemical content of the Dietary Approaches to Stop Hypertension (DASH) diet is higher than in the control study diet. J Am Diet Assoc. 2004;104(11):1725–7.

    Article  PubMed  Google Scholar 

  45. Bazzano LA, Li TY, Joshipura KJ, Hu FB. Intake of fruit, vegetables, and fruit juices and risk of diabetes in women. Diabetes Care. 2008;31(7):1311–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, Van Dam RM, et al. Fruit consumption and risk of type 2 diabetes: Results from three prospective longitudinal cohort studies. BMJ. 2013;347(7923):5001.

    Article  Google Scholar 

  47. Lordan R, Tsoupras A, Mitra B, Zabetakis I. Dairy fats and cardiovascular disease: do we really need to be concerned? Foods. 2018;7(3):1–34.

    Article  CAS  Google Scholar 

  48. Mazidi M, Kengne AP, Mikhailidis DP, Cicero AF, Banach M. Effects of selected dietary constituents on high-sensitivity C-reactive protein levels in U.S. adults. Ann Med. 2018;50(1):1–6.

    Article  CAS  PubMed  Google Scholar 

  49. Astrup A. Yogurt and dairy product consumption to prevent cardiometabolic diseases: epidemiologic and experimental studies. Am J Clin Nutr. 2014;99(5):1235–42.

    Article  CAS  Google Scholar 

  50. Hu FB. Are refined carbohydrates worse than saturated fat? Am J Clin Nutr. 2010;91(6):1541–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. De la Iglesia R, Loria-Kohen V, Zulet MA, Martinez JA, Reglero G, de Molina AR. Dietary strategies implicated in the prevention and treatment of metabolic syndrome. Int J Mol Sci. 2016;17(11):1–21.

    Google Scholar 

Download references

Acknowledgements

We thank the school of Nutritional and Dietetics at Tehran University of medical sciences and participants in this investigation.

Funding

This study was supported by Tehran University of Medical Sciences (Grant ID: 99–3-212–51715).

Author information

Authors and Affiliations

  1. Department of Nutrition, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Alireza Khadem & Rasool Ghaffarian-Ensaf

  2. Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), P.O. Box 14155-6117, Tehran, Iran

    Farideh Shiraseb, Atieh Mirzababaei & Khadijeh Mirzaei

  3. Food Microbiology Research Center, Tehran University of Medical Sciences, Tehran, Iran

    Khadijeh Mirzaei

Authors
  1. Alireza Khadem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farideh Shiraseb
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Atieh Mirzababaei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rasool Ghaffarian-Ensaf
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Khadijeh Mirzaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The project was designed and wrote by Akh and KhM. Data were analyzed by FSH. AM and RGE prepared the manuscript. KhM, supervised overall project. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Khadijeh Mirzaei.

Ethics declarations

Ethics approval and consent to participate

The study protocol has approved by the ethics committee of Tehran University of Medical Sciences (TUMS) with the following identification: IR.TUMS.MEDICINE.REC.1400.710. All methods were carried out in accordance with relevant guidelines and regulations. Each participant was completely informed about the study protocol and provided a written and informed consent form before taking part in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khadem, A., Shiraseb, F., Mirzababaei, A. et al. Association of Lifelines Diet Score (LLDS) and metabolically unhealthy overweight/obesity phenotypes in women: a cross-sectional study. BMC Women's Health 22, 374 (2022). https://doi.org/10.1186/s12905-022-01957-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12905-022-01957-x

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us