Skip to main content
Download PDF

How do carbohydrate quality indices influence on bone mass density in postmenopausal women? A case–control study

BMC Women's Health volume 23, Article number: 42 (2023) Cite this article

Abstract

Background

Carbohydrates are the primary energy source in Asian countries, including Iran. An emerging method can be used to measure the quality of carbohydrates, including the carbohydrate quality index (CQI), which includes a variety of components. Low-carbohydrate diet score (LCDS) has been proposed as a new method of scoring micronutrient intake that could provide a reasonable explanation for the link between diet and the risk of chronic diseases.

Objective

This study aimed to investigate the relationship between CQI, LCDS, glycemic index (GI), glycemic load (GL), insulin load (IL), and insulin index (II) with bone mass density (BMD) in postmenopausal women.

Method

In this case–control study, 131 postmenopausal women with osteoporosis/osteopenia and 131 healthy postmenopausal women aged 45–65 participated. The dual-energy X-ray absorptiometry (DEXA) method measured the BMD of the lumbar vertebrae and femoral neck. A validated semi-quantitative food frequency questionnaire was used to assess dietary intake. Logistic regression were used to evaluate the relation between GI, GL, II, IL, CQI, and LCDS with BMD.

Results

Diets with higher GI increased the risk of osteopenia and osteoporosis, but LCDS and CQI decreased the risk of osteopenia and osteoporosis.

Conclusion

These findings suggest that a higher intake of fruits and vegetables and receiving various dietary vitamins, minerals, and antioxidant compounds may be a useful way to prevent osteopenia in Iranian women.

Peer Review reports

Introduction

Carbohydrates are the primary energy source in Asian countries, including Iran, due to the consumption of high amounts of potatoes, rice, and grains. It has been reported that increased carbohydrate consumption has adverse health effects because of increased energy intake and glycemic load (GL) response [1]. Carbohydrates are known to be the underlying cause of metabolic disorders, while less emphasis is considered on the carbohydrate source. In this line, higher consumption of whole grains, dietary fiber, and cereal fiber and a lower intake of refined grains and added sugar is associated with fewer disorders [2]. Therefore, as an indicator of carbohydrate quality, whole grains, and total dietary fiber have a more important role in determining health than the carbohydrate quantity [2,3,4].

Traditional methods were used to determine the quality of the carbohydrates, including the ratio of total carbohydrates to total dietary fiber (carb-to-fiber) or the ratio of total carbohydrates to cereal fiber (carb-to-cereal), which are associated with an increased risk of metabolic disorders [2]. However, an emerging method can be used to measure the quality of carbohydrates, including the carbohydrate quality index (CQI), which includes a variety of components such as dietary fiber, glycemic index (GI), whole plus refined grain, and solid to total carbohydrate ratio [2, 5]. Various studies have shown that a diet with higher CQI leads to higher-quality carbohydrate intake, which is associated with a reduced risk of metabolic disorders [2, 6,7,8].

As part of a dietary carbohydrate quality classification, the GI classifies the effects of various foods based on their carbohydrate absorption and glycemic response. A diet with a higher GI has been shown to include a higher intake of refined grains and a lower intake of whole grains and cereal fiber, which ultimately leads to an increased risk of various disorders and higher mortality [9]. Also, Shahdadian et al. have reported an association between GI and mortality in women [10]. These effects are associated with elevated glucose and insulin levels in response to higher GI diets. Because higher glucose levels induce oxidative stress and inflammation, decrease osteoblast activity, and increase bone resorption due to the induction of acidosis, adversely affecting bone health [11].

Evidence has shown that a high-carbohydrate diet or more consumption of white rice with a low-fat diet plays an essential role in the development and management of various disorders in Asian countries [1, 12]. In this view, the effects of micronutrient intake on dietary patterns play an important role in assessing the relationship between diet and disease [1]. Low-carbohydrate diet score (LCDS) has been proposed as a new method of scoring micronutrient intake that could provide a reasonable explanation for the link between diet and the risk of chronic diseases [1, 13, 14]. LCDS is associated with lower carbohydrate intake and higher fat and protein intake and defines the ratio of all macronutrients in a dietary pattern [1].

Since no study has been done in this field so far, the purpose of this study was to investigate the relationship between CQI, LCDS, GI, insulin load (IL), and insulin index (II) with bone mass density (BMD) in postmenopausal women.

Methods

Study population

In this case–control study, 131 postmenopausal women with osteoporosis/osteopenia without fracture and 131 healthy postmenopausal women aged 45–65 participated. These women were selected from the Bone Densitometry Center in Isfahan city, Iran, from May 2021 to December 2021. The sample size was computed based on the previous research, considering OR = 2.30 [15]. Menopause was determined as the absence of a menstrual cycle in the last twelve months. The study exclusion criteria included premenopausal, alcohol and glucocorticoid consumption, diabetes, rheumatoid, cancer, renal disease, and history of chemotherapy (Fig. 1). Some detailed of the study have been published previously [16].

Fig. 1
figure 1

Flow chart of the study

Full size image

Participants' body weight was measured with a digital scale and noted with a precision of 100 g. A stadiometer measured height with an accuracy of 0.5 cm, and then body mass index (BMI) was determined. Some information, such as sociodemographic variables, smoking, and use of drugs or supplements, was gathered by a general information questionnaire.

The dual-energy X-ray absorptiometry (DEXA) method measured the BMD of the lumbar vertebrae and femoral neck (the device model was Horizon Wi (S/N 200451)). The status of bone mass was assessed by the World Health Organization (WHO) criteria (T score greater than − 1 means normal bone mass, T score between − 1 and − 2.5 means osteopenia, and T score less than − 2.5 means osteoporosis) [17]. If a physician identified osteoporosis and osteopenia, they were selected for the case samples, and their controls were selected without osteoporosis.

The physical activity level was gathered by the international physical activity questionnaire (IPAQ) [18]. According to metabolic equivalent of task [3]-minutes and standard protocol, women were placed into three groups (less than 600 metabolic equivalent of task (MET)-minutes/week: low activity, 600–3000 MET-minutes/week: moderate activity, and more than 3000 MET-minutes/week: intense activity).

Dietary assessment and food grouping

Participants completed a validated food frequency questionnaire (FFQ) [19]. Baseline dietary intake data which was taken from food composition tables, was used to recognize CQI according to the four criteria: the GI, the ratio of carbohydrates of whole grains to carbohydrates of total grains, the proportion of solid carbohydrates to total carbohydrates, and total dietary fiber intake. Liquid carbohydrates such as fruit juice and sugar-sweetened beverage consumption, while solid carbohydrates were matched to the carbohydrate content of the rest of the meal with each carbohydrate content. The total score range was between 4 and 20 (higher amounts mean better quality of carbohydrates) [7, 20].

For calculating the LCDS, all participants were divided into 11 strata for carbohydrate, refined grains, monounsaturated fatty acid (MUFA), vegetable protein intake, fiber (g/1000 kcal), GL, and polyunsaturated fatty acid (PUFA)(n3/n6) [14]. For MUFA, n3/n6 PUFA, fiber, and vegetable protein, participants in the highest stratum got 10 points and lowest stratum got 0 points. For refined grains, GL and carbohydrates, the intake of the lowest carbohydrate got 10 points and the intake of the highest carbohydrate got 0 points. Finally, the overall diet score was between 0 (the lowest intake of protein and fat and the highest intake of carbohydrates) and 70 (the highest intake of protein and fat and the lowest carbohydrates). So the higher the score, the higher the low-carbohydrate diet pattern named “LCDS”.

GI was calculated from this formula: (GI × available carbohydrate)/total available carbohydrate. Available carbohydrate means total carbohydrate minus fiber [21]. The United States Department of Agriculture food composition table was used for total carbohydrate and fiber content. Iranian GI table was just used for 6 out of 85 foods [21]. For 62 other foods, international tables were used [21], and for 17 foods, we used similar foods because the GI of these foods was not accessible. The GL was calculated from this formula: (total GI × total available carbohydrate/100).

II refers to the increase in insulin level under the curve during 2 h in reaction to 1000 kJ of test food divided by the area under the curve after consumption of 1000 kJ of reference food. Previous studies were used to obtain the II. II of similar foods was used for some items that were not in the list of foods based on the relationship between carbohydrate, fiber, protein, fat, and energy content. For example, raisins were used for dates. To assess IL for each person, IL was calculated (II of the food × energy of one gram of that food × amount of the food eaten) then the IL of each food was summed and then II was calculated (IL/total energy intake) [22,23,24,25].

Ethics statement

The protocols of this study were confirmed by the Ethical Committee of Tabriz University of Medical Sciences (IR.TBZMED.REC.1400.114), and all individuals completed written consents.

Statistical analysis

We used SPSS (version 24.0, SPSS Inc., Chicago IL, USA) for statistical analysis. Also, figure depicted by R software version 3.0.2. A p-value less than 0.05 was considered statistically significant. For evaluating the normal distribution of data, the Kolmogorov–Smirnov test was used. Means and standard deviations (SDs) were used for the continuous variables, and frequencies or percentages were used for the categorical variables. We used an independent samples T-test to analyze of nutrients and food items intake between the case and control.

Moreover, the crude and two adjusted model logistic regression were used to evaluate the relation between BMD and CQI. The effects of BMI and age were controlled in the first adjusted model. In the second model, the role of income, education, physical activity, calcium, and vitamin D supplements were also adjusted.

Results

The baseline characteristics of the participants are presented in Table 1. The age (P = 0.03), the BMD femoral and lumbar (P < 0.001 for both), physical activity level (P = 0.01), education level (P < 0.001), and vitamin D use (P = 0.01) was different between the case and control groups.

Table 1 Baseline characteristics of study participants
Full size table

Based on Table 2, the intake of protein (P = 0.001), fiber (P ˂ 0.001), vitamin A (P = 0.003), K (P = 0.001), B2 (P = 0.005), B3 (P = 0.001), B6 (P ˂ 0.001), B9 (P = 0.01), C (P ˂ 0.001), beta-carotene (P ˂ 0.001), potassium (P = 0.001), iron (P = 0.03), calcium (P = 0.008), magnesium (P = 0.004), zinc (P = 0.04) and copper (P = 0.01) was higher, but PUFA (P = 0.02), omega 6/3 ratio (P = 0.03), and sodium (P = 0.001) was lower in the control group compared to the case group.

Table 2 Nutrient intakes between study participants
Full size table

According to Fig. 2, the intake of fruits (P = 0.001), vegetables (P ˂ 0.001), legumes (P ˂ 0.001), and meat (P = 0.008) was higher, but sweets and sugar beverages (P = 0.01), was lower in the control group compared to case group.

Fig. 2
figure 2

Food group intakes between study participants

Full size image

Table 3 presented the multivariable-adjusted odds ratios and 95% confidence intervals (ORs; 95% CI) for the femoral and lumbar bone density across tertiles (T) of GI, GL, II, IL, CQI, and LCDS. Individuals in the second and last tertile of GI were more likely to have lower femoral density in the crude model (T2-OR 2.55; 95% CI 1.38–4.64 and T3-OR 2.16; 95% CI 1.18–3.97) and both adjusted models (Model 1: T2-OR 2.49; 95% CI 1.34–4.64 and T3-OR 2.22; 95% CI 1.20–4.13; and Model 2: T2-OR 2.59; 95% CI 1.35–4.96 and T3-OR 2.02; 95% CI 1.06–3.87) compared those in the lowest tertile. Also, a positive association was seen between the second and last tertile of GI with the lumbar density in the crude model (T2-OR 2.26; 95% CI 1.23–4.14 and T3-OR 3.10; 95% CI 1.67–5.67) and both adjusted models (Model 1: T2-OR 2.21; 95% CI 1.20–4.08 and T3-OR 3.18; 95% CI 1.70–5.93; and Model 2: T2-OR 2.13; 95% CI: 1.13–4.01 and T3-OR 3.06; 95% CI 1.60–5.84).

Table 3 Crude and multivariable-adjusted odds ratios and 95% CIs across tertile of GI, GL, II, IL, CQI and LCDS
Full size table

Moreover, we observed a significant negative relationship between CQI with the femoral BMD in the crude (OR 0.54; 95% CI 0.29–0.99) and the first adjusted model (OR 0.49; 95% CI 0.26–0.91). However, for the lumbar BMD, the association was significant in the second and last tertile of CQI compared to the first tertile in the crude model (T2-OR 0.34; 95% CI 0.18–0.63 and T3-OR 0.28; 95% CI 0.15–0.52) and both adjusted models (Model 1: T2-OR 0.33; 95% CI 0.18–0.62 and T3-OR 0.26; 95% CI 0.13–0.49; and Model 2: T2-OR 030; 95% CI 0.15–0.58 and T3-OR 0.26; 95% CI 0.13–0.50). Also, we observed a negative relationship between the last tertile of LCDS with the lumbar BMD in the crude model (OR 0.42; 95% CI 0.23–0.77) and both adjusted models (Model 1-OR 0.37; 95% CI 0.19–0.69; Model 2-OR 0.36; 95% CI 0.19–0.70) compared with those on the lowest tertile.

Discussion

In this case–control study, our results showed that postmenopausal women with osteoporosis/osteopenia in Iran received less fruits, vegetables, vitamins and minerals. A higher GI intake and subsequently a lower CQI diet was also demonstrated by women with osteoporosis/osteopenia.

As a major energy source, carbohydrates play an essential role in bone metabolism. In this regard, the amount of carbohydrate and the quality of carbohydrate are considered important factors [26]. The dietary GI was presented by Jenkins et al. in 1981. This index was defined as glucose available after food digestion, which was used to measure carbohydrate quality [27].

The GI, as a component of CQI, affects glucose fluctuations. Therefore, CQI was associated with insulin resistance. Consumption of foods with high GI by reducing blood glucose fluctuations leads to enhanced appetite and energy intake. However, a low GI diet declined appetite and energy intake by increasing fluctuations in blood glucose [5]. This view demonstrated that eating foods with a high GI increase fat stores by reducing fat oxidation and increasing carbohydrate oxidation [5, 28, 29]. Hence, it has been shown that increasing fat stores by releasing pro-inflammatory cytokines, free fatty acids, and reactive oxygen species leads to inflammatory conditions that contribute to insulin resistance [30]. In addition, high GI sweetened beverages are associated with increased blood glucose levels and insulin resistance [5], and through fructose, they help increase fat storage by stimulating lipogenesis, increasing triacylglycerol accumulation, fat hypertrophy, and eventually insulin resistance [1]. Elevated blood glucose following the consumption of foods with high GI causes an increased need for insulin secretion and impaired beta cell function and glucose metabolism [1, 31]. Hence, enhanced glucose levels with a negative effect on osteoblast and osteoclast function, including overstimulation of the insulin signaling pathway and inhibitory effect on osteoblast cells, lead to abnormal bone metabolism and subsequent increase in fractures [11, 32, 33]. By stimulating the production of advanced glycation end products [34], hyperglycemia leads to increased cross-link between collagen, bone fractures, cell apoptosis, and inflammation [11]. In contrast, the consumption of whole grains with low GI reduces blood glucose levels and insulin resistance by reducing digestion and starch absorption [5, 35, 36]. Therefore, it seems that a low GI diet can reverse hyperglycemia, insulin resistance, inflammation, and oxidative stress, followed by bone health explains the findings of the study by Garcia-Gavilan et al. [11] which is consistent with our findings.

In the present study, the quantity of carbohydrates by determining LCDS was considered in addition to the quality of carbohydrates. A LCDS is associated with a reduction in carbohydrate intake and an increase in fat and protein intake. Higher LCDS have been reported to be associated with lower carbohydrate intake obtained from whole grains, fruits, and vegetables, and reduced intake of simple sugars, especially fructose [1]. In the Iranian population, a decrease in fruit and vegetable intake and an increase in simple sugar intake have also been reported [5, 37]. It has also been shown that in Iranians, more than 60% of total energy is obtained from carbohydrates, especially refined carbohydrates with a high GI [5]. Hence, higher LCD scores have been suggested to have beneficial health effects by replacing low GI fruits and vegetables, whole grains, and healthy sources of fat and protein with high GI refined carbohydrates [38]. These findings explain our results about how the higher LCDS associated with the higher BMD and thereby prevents osteopenia.

Observational studies have shown that a higher intake of fruits and vegetables is associated with increased BMD and decreased bone loss and fracture rate [39,40,41]. Lin et al. reported that receiving the Dietary Approaches to Stop Hypertension (DASH) diet for three months was associated with an increased intake of fruits and vegetables, significantly reduced bone turnover markers in 76-year-old men and women [42]. Our findings are consistent with these results and support the hypothesis that a higher intake of fruits and vegetables may be benefit for bone health. Mechanisms related to the effect of fruits and vegetables on bone health include the following:

First, providing a favorable ratio of sodium to potassium intake and reducing the diet's acidity, and thus reducing hypercalciuria [43]. The acid–base hypothesis has been reported that bone tissue buffered acid loading, leading to bone resorption and reduced bone density [43]. Because of their richness in alkaline ions such as potassium, calcium, and magnesium, fruits and vegetables cause alkaline conditions. It has been shown that all three elements have a beneficial effect on bone health [39]. A recent meta-analysis has shown that the richness of fruits and vegetables from these alkaline precursors counteracts the effects of dietary-derived calciuria [44, 45]. From this perspective, a meta-analysis of 17 studies reported that adequate calcium intake of 1200 mg/day is associated with a reduction in BMD loss in postmenopausal women, followed by a reduced fracture risk [46, 47].

In contrast, severe dietary calcium restriction leads to severe hypercalciuria and progressive loss of BMD [46]. Regarding the effect of magnesium on bone health, it has been shown that it leads to a significant increase in osteoblast survival, alkaline phosphatase activity, and osteocalcin levels [46, 48]. Therefore, insufficient magnesium intake is associated with osteopenia [46].

Second, they are rich in vitamin K, which plays a crucial role in bone health due to the gamma-carboxylation of osteocalcin [39, 44, 49]. Vitamin K plays an important role as a cofactor of enzymes involved in bone metabolism by increasing many bone formation markers such as alkaline phosphatase and insulin-like growth factor 1 (IGF-1) through osteoblast differentiation and by regulating extracellular matrix mineralization [46, 50,51,52]. The role of vitamin K in bone health has been reported in various studies [46, 53, 54].

Third, receiving antioxidants such as vitamin C, beta-carotene, and other carotenoids such as lutein, xanthine, and lycopene, which due to their antioxidant properties, have a protective role against oxidative stress [39]. In animal models, the protective effect of beta-carotene against bone loss has also been shown [46, 55]. Vitamin C may affect bone mass through the hydroxylation of proline and lysine, which is required to form triple helix collagen [44]. In addition, vitamin C has a protective effect against bone fractures by stimulating collagen synthesis types 1 and 3, while vitamin C deficiency leads to osteoclastogenesis and subsequent bone resorption. Adequate vitamin C intake and improved bone health can be a preventive tool against osteopenia and osteoporosis [46].

Fourth, receiving polyphenols, carotenoids, tocopherols, vitamin K, and glutathione, except for the antioxidant effects due to up-regulating Runt-related transcription factor 2 (Runx2), Osterix, and IGF-1 along with increasing the expression of lysyl oxidase, have beneficial effects on bone health [39].

Fifth, being rich in B vitamins can have a desirable effect on bone health. Adequate vitamin B6 intake has a positive effect on bone health, directly affecting bone metabolism and indirectly modulating steroid hormone receptors such as estrogen [46, 56]. The role of B9 in maintaining bone health has also been shown [46].

This study is the first one that assessed the relationship between carbohydrate quality indices and BMD in postmenopausal women. Also, we controlled the effect of several confounding factors in order to obtain more accurate results. However, this case–control study had some limitations: selection and recall bias that affect association is probable for the design of the study. Misclassification of the dietary intake of participants due to the use of FFQ cannot be omitted; however, we used validated FFQ, and finally, we were not capable of doing stratified analysis for a limited sample size.

In conclusion, higher GI diets increased the risk, but LCD and CQI decreased the risk of osteopenia and osteoporosis. These findings suggest that a higher intake of fruits and vegetables and receiving various dietary vitamins, minerals, and antioxidant compounds may be a useful way to prevent osteopenia and osteoporosis in Iranian women. Also, further studies with a longitudinal design, particularly trial studies and a higher sample size, are required to explain the better association between CQI and osteoporosis risk among this population.

Availability of data and materials

Data are available through a reasonable request from the corresponding author.

References

  1. Sangsefidi ZS, Lorzadeh E, Nadjarzadeh A, Mirzaei M, Hosseinzadeh M. The association between low-carbohydrate diet score and metabolic syndrome among Iranian adults. Public Health Nutr. 2021;24(18):6299–308.

    Article  Google Scholar 

  2. Sawicki CM, Lichtenstein AH, Rogers GT, Jacques PF, Ma J, Saltzman E, McKeown NM. Comparison of indices of carbohydrate quality and food sources of dietary fiber on longitudinal changes in waist circumference in the Framingham offspring cohort. Nutrients. 2021;13(3):997.

    Article  CAS  Google Scholar 

  3. Reynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L. Carbohydrate quality and human health: a series of systematic reviews and meta-analyses. The Lancet. 2019;393(10170):434–45.

    Article  CAS  Google Scholar 

  4. Willett WC, Liu S. Carbohydrate quality and health: distilling simple truths from complexity, vol. 110. Oxford: Oxford University Press; 2019. p. 803–4.

    Google Scholar 

  5. Janbozorgi N, Djafarian K, Mohammadpour S, ZareieAbyane M, Zameni M, Badeli M, Akbarzade Z, Clark CT, Shab-Bidar S. Association between carbohydrate quality index and general and central obesity in adults: a population-based study in Iran. J Cardiovasc Thorac Res. 2021;13(4):298–308.

    Article  Google Scholar 

  6. Santiago S, Zazpe I, Bes-Rastrollo M, Sánchez-Tainta A, Sayón-Orea C, de la Fuente-Arrillaga C, Benito S, Martínez JA, Martínez-González M. Carbohydrate quality, weight change and incident obesity in a Mediterranean cohort: the SUN Project. Eur J Clin Nutr. 2015;69(3):297–302.

    Article  CAS  Google Scholar 

  7. Zazpe I, Santiago S, Gea A, Ruiz-Canela M, Carlos S, Bes-Rastrollo M, Martínez-González MA. Association between a dietary carbohydrate index and cardiovascular disease in the SUN (Seguimiento Universidad de Navarra) Project. Nutr Metab Cardiovasc Dis. 2016;26(11):1048–56.

    Article  CAS  Google Scholar 

  8. Martínez-González MA, Fernandez-Lazaro CI, Toledo E, Díaz-López A, Corella D, Goday A, Romaguera D, Vioque J, Alonso-Gómez ÁM, Wärnberg J, et al. Carbohydrate quality changes and concurrent changes in cardiovascular risk factors: a longitudinal analysis in the PREDIMED-Plus randomized trial. Am J Clin Nutr. 2020;111(2):291–306.

    Article  Google Scholar 

  9. Hardy DS, Garvin JT, Xu H. Carbohydrate quality, glycemic index, glycemic load and cardiometabolic risks in the US, Europe and Asia: a dose-response meta-analysis. Nutr Metab Cardiovasc Dis. 2020;30(6):853–71.

    Article  CAS  Google Scholar 

  10. Shahdadian F, Saneei P, Milajerdi A, Esmaillzadeh A. Dietary glycemic index, glycemic load, and risk of mortality from all causes and cardiovascular diseases: a systematic review and dose–response meta-analysis of prospective cohort studies. Am J Clin Nutr. 2019;110(4):921–37.

    Article  Google Scholar 

  11. García-Gavilán JF, Bulló M, Camacho-Barcia L, Rosique-Esteban N, Hernández-Alonso P, Basora J, Martínez-González MA, Estruch R, Fitó M, Salas-Salvadó J. Higher dietary glycemic index and glycemic load values increase the risk of osteoporotic fracture in the PREvención con DIeta MEDiterránea (PREDIMED)-Reus trial. Am J Clin Nutr. 2018;107(6):1035–42.

    Article  Google Scholar 

  12. Ha K, Joung H, Song Y. Low-carbohydrate diet and the risk of metabolic syndrome in Korean adults. Nutr Metab Cardiovasc Dis. 2018;28(11):1122–32.

    Article  CAS  Google Scholar 

  13. de Koning L, Fung TT, Liao X, Chiuve SE, Rimm EB, Willett WC, Spiegelman D, Hu FB. Low-carbohydrate diet scores and risk of type 2 diabetes in men. Am J Clin Nutr. 2011;93(4):844–50.

    Article  Google Scholar 

  14. Halton TL, Willett WC, Liu S, Manson JE, Albert CM, Rexrode K, Hu FB. Low-carbohydrate-diet score and the risk of coronary heart disease in women. N Engl J Med. 2006;355(19):1991–2002.

    Article  CAS  Google Scholar 

  15. Shivappa N, Hébert JR, Karamati M, Shariati-Bafghi S-E, Rashidkhani B. Increased inflammatory potential of diet is associated with bone mineral density among postmenopausal women in Iran. Eur J Nutr. 2016;55(2):561–8.

    Article  CAS  Google Scholar 

  16. Ghadiri M, Cheshmazar E, Shateri Z, Gerami S, Nouri M, PourghassemGargari B. Healthy plant-based diet index as a determinant of bone mineral density in osteoporotic postmenopausal women: a case-control study. Front Nutr. 2023;9:1083685.

    Article  Google Scholar 

  17. Nuti R, Brandi ML, Checchia G, Di Munno O, Dominguez L, Falaschi P, Fiore CE, Iolascon G, Maggi S, Michieli R, et al. Guidelines for the management of osteoporosis and fragility fractures. Intern Emerg Med. 2019;14(1):85–102.

    Article  Google Scholar 

  18. Collaborators GO. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017;377(1):13–27.

    Article  Google Scholar 

  19. Mirmiran P, Esfahani FH, 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  Google Scholar 

  20. Farazi M, Jayedi A, Noruzi Z, Firouzabadi FD, Asgari E, Djafarian K, Shab-Bidar S. The association between carbohydrate quality index and nutrient adequacy in Iranian adults. Nutr Food Sci. 2021;51:1113–23.

    Article  Google Scholar 

  21. Foster-Powell K, Holt SH, Brand-Miller JC. International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr. 2002;76(1):5–56.

    Article  CAS  Google Scholar 

  22. Bell KJ, Petocz P, Colagiuri S, Brand-Miller JC. Algorithms to improve the prediction of postprandial insulinaemia in response to common foods. Nutrients. 2016;8(4):210.

    Article  Google Scholar 

  23. Bao J, Atkinson F, Petocz P, Willett WC, Brand-Miller JC. Prediction of postprandial glycemia and insulinemia in lean, young, healthy adults: glycemic load compared with carbohydrate content alone. Am J Clin Nutr. 2011;93(5):984–96.

    Article  CAS  Google Scholar 

  24. Holt S, Miller J, Petocz P. An insulin index of foods: the insulin demand generated by 1000-kJ portions of common foods. Am J Clin Nutr. 1997;66(5):1264–76.

    Article  CAS  Google Scholar 

  25. Sadeghi O, Hasani H, Mozaffari-Khosravi H, Maleki V, Lotfi MH, Mirzaei M. Dietary insulin index and dietary insulin load in relation to metabolic syndrome: the Shahedieh cohort study. J Acad Nutr Diet. 2020;120(10):1672–86.

    Article  Google Scholar 

  26. Bueno-Vargas P, Manzano M, Pérez-Castillo ÍM, Rueda R, López-Pedrosa JM. Dietary complex and slow digestive carbohydrates promote bone mass and improve bone microarchitecture during catch-up growth in rats. Nutrients. 2022;14(6):1303.

    Article  CAS  Google Scholar 

  27. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, Baldwin JM, Bowling AC, Newman HC, Jenkins AL, Goff DV. Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr. 1981;34(3):362–6.

    Article  CAS  Google Scholar 

  28. Brand-Miller JC, Holt SH, Pawlak DB, McMillan J. Glycemic index and obesity. Am J Clin Nutr. 2002;76(1):281s–5s.

    Article  CAS  Google Scholar 

  29. Livesey G. Low-glycaemic diets and health: implications for obesity. Proc Nutr Soc. 2005;64(1):105–13.

    Article  CAS  Google Scholar 

  30. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed Pharmacother. 2021;137:111315.

    Article  CAS  Google Scholar 

  31. Barclay AW, Petocz P, McMillan-Price J, Flood VM, Prvan T, Mitchell P, Brand-Miller JC. Glycemic index, glycemic load, and chronic disease risk: a meta-analysis of observational studies. Am J Clin Nutr. 2008;87(3):627–37.

    Article  CAS  Google Scholar 

  32. Ghodsi M, Larijani B, Keshtkar AA, Nasli-Esfahani E, Alatab S, Mohajeri-Tehrani MR. Mechanisms involved in altered bone metabolism in diabetes: a narrative review. J Diabetes Metab Disord. 2016;15:52.

    Article  Google Scholar 

  33. Cunha JS, Ferreira VM, Maquigussa E, Naves MA, Boim MA. Effects of high glucose and high insulin concentrations on osteoblast function in vitro. Cell Tissue Res. 2014;358(1):249–56.

    Article  CAS  Google Scholar 

  34. Mercier R, Perron J, Weisnagel SJ, Robitaille J. Associations between fruit and vegetables intake and abnormal glucose tolerance among women with prior gestational diabetes mellitus. Eur J Nutr. 2019;58(2):689–96.

    Article  CAS  Google Scholar 

  35. Cho SS, Qi L, Fahey GC Jr, Klurfeld DM. Consumption of cereal fiber, mixtures of whole grains and bran, and whole grains and risk reduction in type 2 diabetes, obesity, and cardiovascular disease. Am J Clin Nutr. 2013;98(2):594–619.

    Article  CAS  Google Scholar 

  36. Wanders AJ, van den Borne JJ, de Graaf C, Hulshof T, Jonathan MC, Kristensen M, Mars M, Schols HA, Feskens EJ. Effects of dietary fibre on subjective appetite, energy intake and body weight: a systematic review of randomized controlled trials. Obes Rev. 2011;12(9):724–39.

    CAS  Google Scholar 

  37. Esteghamati A, Noshad S, Nazeri A, Khalilzadeh O, Khalili M, Nakhjavani M. Patterns of fruit and vegetable consumption among Iranian adults: a SuRFNCD-2007 study. Br J Nutr. 2012;108(1):177–81.

    Article  CAS  Google Scholar 

  38. Halton TL, Liu S, Manson JE, Hu FB. Low-carbohydrate-diet score and risk of type 2 diabetes in women. Am J Clin Nutr. 2008;87(2):339–46.

    Article  CAS  Google Scholar 

  39. Xie HL, Wu BH, Xue WQ, He MG, Fan F, Ouyang WF, Tu SL, Zhu HL, Chen YM. Greater intake of fruit and vegetables is associated with a lower risk of osteoporotic hip fractures in elderly Chinese: a 1:1 matched case-control study. Osteoporos Int. 2013;24(11):2827–36.

    Article  CAS  Google Scholar 

  40. Monma Y, Niu K, Iwasaki K, Tomita N, Nakaya N, Hozawa A, Kuriyama S, Takayama S, Seki T, Takeda T, et al. Dietary patterns associated with fall-related fracture in elderly Japanese: a population based prospective study. BMC Geriatr. 2010;10:31.

    Article  Google Scholar 

  41. Macdonald HM, Black AJ, Aucott L, Duthie G, Duthie S, Sandison R, Hardcastle AC, Lanham New SA, Fraser WD, Reid DM. Effect of potassium citrate supplementation or increased fruit and vegetable intake on bone metabolism in healthy postmenopausal women: a randomized controlled trial. Am J Clin Nutr. 2008;88(2):465–74.

    Article  CAS  Google Scholar 

  42. Lin PH, Ginty F, Appel LJ, Aickin M, Bohannon A, Garnero P, Barclay D, Svetkey LP. The DASH diet and sodium reduction improve markers of bone turnover and calcium metabolism in adults. J Nutr. 2003;133(10):3130–6.

    Article  CAS  Google Scholar 

  43. Gunn CA, Weber JL, McGill AT, Kruger MC. Increased intake of selected vegetables, herbs and fruit may reduce bone turnover in post-menopausal women. Nutrients. 2015;7(4):2499–517.

    Article  Google Scholar 

  44. Qiu R, Cao WT, Tian HY, He J, Chen GD, Chen YM. Greater intake of fruit and vegetables is associated with greater bone mineral density and lower osteoporosis risk in middle-aged and elderly adults. PLoS ONE. 2017;12(1): e0168906.

    Article  Google Scholar 

  45. Lambert H, Frassetto L, Moore JB, Torgerson D, Gannon R, Burckhardt P, Lanham-New S. The effect of supplementation with alkaline potassium salts on bone metabolism: a meta-analysis. Osteoporos Int. 2015;26(4):1311–8.

    Article  CAS  Google Scholar 

  46. Rondanelli M, Faliva MA, Barrile GC, Cavioni A, Mansueto F, Mazzola G, Oberto L, Patelli Z, Pirola M, Tartara A, et al. Nutrition, physical activity, and dietary supplementation to prevent bone mineral density loss: a food pyramid. Nutrients. 2022;14(1):74.

    Article  CAS  Google Scholar 

  47. Wu J, Xu L, Lv Y, Dong L, Zheng Q, Li L. Quantitative analysis of efficacy and associated factors of calcium intake on bone mineral density in postmenopausal women. Osteoporos Int. 2017;28(6):2003–10.

    Article  CAS  Google Scholar 

  48. He LY, Zhang XM, Liu B, Tian Y, Ma WH. Effect of magnesium ion on human osteoblast activity. Braz J Med Biol Res 2016, 49(7)

  49. Cockayne S, Adamson J, Lanham-New S, Shearer MJ, Gilbody S, Torgerson DJ. Vitamin K and the prevention of fractures: systematic review and meta-analysis of randomized controlled trials. Arch Intern Med. 2006;166(12):1256–61.

    Article  CAS  Google Scholar 

  50. Palermo A, Tuccinardi D, D’Onofrio L, Watanabe M, Maggi D, Maurizi AR, Greto V, Buzzetti R, Napoli N, Pozzilli P, et al. Vitamin K and osteoporosis: myth or reality? Metab Clin Exp. 2017;70:57–71.

    Article  CAS  Google Scholar 

  51. Akbari S, Rasouli-Ghahroudi AA. Vitamin K and bone metabolism: a review of the latest evidence in preclinical studies. Biomed Res Int. 2018;2018:4629383.

    Article  Google Scholar 

  52. Iwamoto J. Vitamin K2 therapy for postmenopausal osteoporosis. Nutrients. 2014;6(5):1971–80.

    Article  Google Scholar 

  53. Shah K, Gleason L, Villareal DT. Vitamin K and bone health in older adults. J Nutr Gerontol Geriatr. 2014;33(1):10–22.

    Article  Google Scholar 

  54. Inaba N, Sato T, Yamashita T. Low-dose daily intake of vitamin K(2) (menaquinone-7) improves osteocalcin γ-carboxylation: a double-blind, randomized controlled trials. J Nutr Sci Vitaminol. 2015;61(6):471–80.

    Article  CAS  Google Scholar 

  55. Yee MMF, Chin KY, Ima-Nirwana S, Wong SK. Vitamin A and bone health: a review on current evidence. Molecules (Basel, Switzerland). 2021;26(6):1757.

    Article  CAS  Google Scholar 

  56. Dai Z, Wang R, Ang LW, Yuan JM, Koh WP. Dietary B vitamin intake and risk of hip fracture: the Singapore Chinese Health Study. Osteoporos Int. 2013;24(7):2049–59.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Tabriz University of Medical Sciences, Tabriz, Iran, and to all the participants.

Funding

The present study was carried out with the funding of the Nutrition Research Center of Tabriz University of Medical Sciences (Grant Number Pazhoohan Code: 66934).

Author information

Authors and Affiliations

  1. Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran

    Mehran Nouri

  2. Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

    Mehran Nouri, Marzieh Mahmoodi, Milad Rajabzadeh-Dehkordi & Mohebat Vali

  3. Department of Community Nutrition, School of Nutrition and Food Science, Shiraz University of Medical Sciences, Shiraz, Iran

    Mehran Nouri & Milad Rajabzadeh-Dehkordi

  4. Nutrition Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Marzieh Mahmoodi

  5. Student Research Committee, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Zainab Shateri

  6. Student Research Committee, Department of Biochemistry and Diet Therapy, Faculty of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

    Marzieh Ghadiri

  7. Nutrition Research Center, Department of Biochemistry and Diet Therapy, Faculty of Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran

    Bahram Pourghassem Gargari

Authors
  1. Mehran Nouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marzieh Mahmoodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zainab Shateri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marzieh Ghadiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Milad Rajabzadeh-Dehkordi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohebat Vali
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bahram Pourghassem Gargari
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MN, MM, MG, MRD, MV, and ZS; contributed to data collection, writing, and editing of the draft. MN and BPG; contributed to all data and statistical analysis. BPG contributed to funding acquisition and project administration. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Bahram Pourghassem Gargari.

Ethics declarations

Ethical approval and consent to participate

The present study was approved by the Research Ethics Committee of Tabriz University of Medical Sciences, Tabriz, Iran (IR.TBZMED.REC.1400.114) and the informed consents were completed by all participants. In the current study, all performed methods were in accordance with the Helsinki Declaration.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have 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

Nouri, M., Mahmoodi, M., Shateri, Z. et al. How do carbohydrate quality indices influence on bone mass density in postmenopausal women? A case–control study. BMC Women's Health 23, 42 (2023). https://doi.org/10.1186/s12905-023-02188-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12905-023-02188-4

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us