Skip to main content
Download PDF

Association between dietary insulin index and postmenopausal osteoporosis in Iranian women: a case-control study

BMC Women's Health volume 24, Article number: 401 (2024) Cite this article

Abstract

Background

The relationship between the dietary insulin index (DII) and the disease’s risk is unknown, despite the fact that hyperinsulinemia is presumed to contribute to osteoporosis. The insulin response of various diets determines the DII. This study aimed to investigate the connection between postmenopausal Iranian women’s adherence to a diet with a higher insulinemic potential and osteoporosis.

Methods

A total of 380 postmenopausal women were included in the current case-control study. A 168-item food frequency questionnaire (FFQ) with established validity and reliability was used to evaluate individuals’ daily calorie intake. The standard formula was employed to determine the dietary insulin load of each product. Subsequently, the calculation of DII was performed by dividing the dietary insulin load by the total energy consumed for each individual. In order to investigate the relationship between osteoporosis and DII, logistic regression was implemented.

Results

The results of the current study demonstrated a substantial inverse relationship between osteoporosis and the DII, even after accounting for confounding variables (OR = 0.927; 95% CI = 0.888–0.967). The mean scores of DII (P < 0.001) was significantly higher in control group (36.82 ± 8.98) compared to the case group (33.53 ± 6.28).

Conclusions

Our findings suggest that keeping a diet high in insulin index and low in foods that are insulinogenic may improve bone mass density. Consequently, it may be essential for postmenopausal women to consume nutrients that stimulate insulin production in order to prevent osteoporosis.

Peer Review reports

Introduction

The most common bone disease is osteoporosis [1], which causes a reduction in bone mineral content and an increase in bone fractures [2]. One in three women and one in five men over the age of 50 are at risk of fractures associated to osteoporosis, according to a research from the International Osteoporosis Foundation [3]. National Osteoporosis Prevention, Diagnosis, and Treatment Program in Iran has revealed that 50% of men and 70% of women over the age of 50 are affected by osteoporosis and osteopenia [4]. This disease is four times more prevalent in women during menopause than in men [5]. After menopause, Iranian women exhibit osteopenia and osteoporosis of lumbar spine at rates of 50% and 26.7%, respectively [6].

Previous research demonstrated that insulin could have anabolic effects on bone [7]. Additionally, serum glucose and insulin concentrations were positively correlated with bone mass and a reduced fracture rate [8]. According to the findings of some research, insulin hypersecretion and postprandial hyperinsulinemia may eventually develop in insulin resistance (IR) [9], which may then benefit bone mineral density (BMD) [8, 10, 11]. IR and BMD do, however, have a complex relationship [12]. Previous research [13] showed that there was a direct correlation between a higher IR and a higher dietary insulin index. The results of this study indicated that the Iranian population’s IR can be elevated by insulinogenic foods, such as rice and bread, which comprise the majority of their diet [13].

The postprandial insulin secretion of a variety of meals was estimated using the food insulin index (FII) [14]. In fact, the dietary insulin index (DII) may be computed using this metric [15]. Compared to the glycemic index (GI), which only communicates specific information about the carbohydrate content of meals and glycemic response [16], the DII is more appropriate for explaining the emergence of chronic diseases since it is accurately calculated based on insulin response [13]. Furthermore, the GI failed to effectively reflect the insulin response of a large number of meal components [16], but DII predicted insulin responses to mixed meals more accurately. There are contradicting studies regarding the impact of IR on BMD. Although certain studies have demonstrated a positive correlation between IR and BMD [8, 10, 11], others have failed a significant association between them [17]. The conflicting results compelled us to reassess their correlation. The objective of this study was to examine the correlation between Dietary Inflammatory Index (DII) and the susceptibility to osteoporosis in postmenopausal women from Iran. Because DII is properly determined based on insulin response rather than other mediators, it is more appropriate indicator than GI to predict the probabilities of chronic illness [18]. We postulated that insulin may stimulate bone growth and an increase in bone density. In the case that a significant negative association between DII and the incidence of osteoporosis were discovered, we would conclude that eating meals with high insulin index would be essential for postmenopausal women to avoid osteoporosis.

Methods

Study population

This case-control study recruited 380 postmenopausal women (190 cases and 190 controls) between the ages of 45 and 85 who were referred to Shariati hospital and several private clinics in Tehran, Iran. The rheumatology specialist’s diagnosis was established on the basis of the identification of osteoporosis using dual-energy X-ray absorptiometry [19]. The control group was selected from the visitors and patients’ companions who traveled to these institutions from various locations in Tehran and did not have any familial ties to the patients. Cases and controls were age- matched, and the participants in both groups were women who were postmenopausal, defined as having not had a menstrual period for at least 12 months. All participants refrained from using any drugs or dietary supplements that affect bone metabolism, including glucocorticoids, calcitonin, thyroxin, anticoagulants, antacids, calcium (500 mg/day), vitamin D (15 IU/day), glucosamine, or bisphosphonates. Additionally, they have not been diagnosed with any conditions that might affect their BMD status, such as renal, gastrointestinal, endocrine, or, rheumatic disorders. Moreover, they followed no specific diet throughout the previous year. Those who reported an inappropriate energy intake (< 800 kcal/day or > 4200 kcal/day) and those who did not answer more than 20% of the food frequency questionnaire (FFQ) items were excluded [20]. The ethics committee of the Islamic Azad University’s Science and Research Branch in Tehran, Iran, gave its approval for this work (IR.IAU.SRB.REC.1396.119).

Study variables

Body weight was measured with a digital scale (Tefal) with an accuracy of 0.1 kg while wearing light clothes, and height was measured using a tape meter with an accuracy of 0.1 cm. The anthropometric measurements were body mass index (BMI), which was determined using the following formula: BMI, weight (kg), and height (cm). BMI is equal to body weight (kg)/ [height (m)]2. The survey collected information on many factors like age (in years), level of education (under graduate/ graduate/ post graduate), breastfeeding status (yes/no), alcohol consumption (yes/no), weight (in kilogram), duration of lactation (in weeks), and use of oral contraceptives (yes/no). Additionally, a valid questionnaire [21] was implemented to evaluate the extent of physical activity. Its validity was verified by the “CSA Accelerometer Ambulatory Monitor” system (Model 7164) and the Daily Activity Questionnaire, which were administered to 2500 Danish men and women between the ages of 20 and 60 [21]. The validity and dependability of the calculation were verified in Iranian women [22], and the amount of physical activity was determined using metabolic equivalent hours per week [21]. According to their levels of physical activity, participants in the present research were divided into three groups: very low (less than 600 MET-minutes per week), low (between 600 and 3000 MET-minutes per week), moderate and high (greater than 3000 MET- minutes per week) [23]. Participants’ dietary consumption was recorded using a 168-item FFQ, which is a valid and trustworthy tool [24, 25]. Next, the frequency of intake for each food item in the FFQ was translated to grams per day using household measurements [26]. Subsequently, the Nutritionist IV program was used to compute the quantity of macronutrients and micronutrients consumed.

Assessment of dietary insulin index

By dividing the area under the insulin response curve for 1000 kcal of the test meal after two hours by the area under the curve for 1000 kcal of glucose, the reference food, the food insulin index (FII) was determined. The insulin index of 68 food items was obtained from previous research [14, 27, 28]. Tea, coffee, and sodium were allocated an insulin index of 0 as a result of their negligible caloric and macronutrient levels. Additionally, the food insulin index of comparable items was implemented in the event that specific items were not included in the designated food list. For example, dates and raisins, which are classified as dried fruits, have comparable energy, carbohydrate, fat, protein, and fiber profiles. As a result, for dates, we used the insulin index of raisins as a guide. First, we used the following formula to determine the insulin load of each meal: Insulin load of a certain food = Insulin index of the food × calorie content per 1 g of that food × quantity of that food eaten [29]. The DIL for each person was then determined by adding up the insulin load of every dietary item. We further calculated each person’s DII by dividing DIL by total energy used.

Sample size calculation

A total of 176 samples were generated using GPower 3.1.9.2 (Kiel University, Germany) to analyze the sample size with α = 0.05, 95% power (β = 0.05), and an effect size of 0.1. In order to account for a 10% dropout rate, each group consisted of 190 people.

Statistical analysis

All statistical analyses were conducted using SPSS software, version 26, from the IBM Corporation, Amonk, NY, USA. The independent sample t-test was used to evaluate the mean of normally distributed qualitative variables across two groups, and one-way ANOVA was utilized to compare the mean of normally distributed quantitative variables across more than two groups. Furthermore, we employed the Mann-Whitney test to investigate the differences in non-normal variables between the two groups. The Chi-squared test was implemented to evaluate the correlation between categorical variables in the control and case groups. The frequency distribution indices were employed to elucidate the qualitative data, while the mean and standard deviation (SD) were computed to represent quantitative variables. The odds ratio (OR) and 95% confidence intervals (CI) were calculated using binary logistic regression after adjusting for physical activity, BMI, and alcohol use to assess the relationship between the DII and osteoporosis. All analyses were considered significant when P < 0.05.

Results

The demographic differences between the case and control groups are shown in Table 1. There was no discernible difference in the mean age of the two groups (55.99 ± 6.73 vs. 55.58 ± 6.07 years, P = 0.533). The mean DII in the control group was much greater (P < 0.001). The mean DII score for the case group was 33.53 ± 6.28, whereas the control group’s was 36.82 ± 8.98. Furthermore, the control group’s mean values for breastfeeding duration (P = 0.042) and physical activity (P < 0.001) were much greater. The control group’s weight was lower (P = 0.013), but their BMI was not different (P = 0.139). The alcohol consumption of cases was greater than that of controls (P < 0.001). The results shown in Table 2 show that there was no difference in the baseline characteristics across DII tertiles.

Table 1 Demographic characteristics of the participants in the case and control groups
Full size table
Table 2 General characteristics of study participants in different tertiles (T) of the DII
Full size table

The average consumption of nutrients in the two groups is shown Table 3. The control group exhibits considerably higher mean intakes of antioxidant vitamins and minerals, including vitamin A (P = 0.026), selenium (P = 0.002), alpha-carotene, beta-cryptoxanthin, vitamin C, zinc, and manganese (P < 0.001).

Table 3 Daily intake of nutrients in the case and control group
Full size table

Table 4 describes the nutritional consumption among the various DII tertiles. Table 4 shows that there are no appreciable variations in the majority of nutrient intakes between the top and lowest tertiles of the DII.

Table 4 Dietary intake of nutrients in different tertiles (T) of the DII
Full size table

After accounting for physical activity, weight, the duration of lactation, and alcohol consumption, Table 5 demonstrates a significant negative correlation between the DII and osteoporosis. The risk of osteoporosis decreased at a rate of 7% per unit increase in the DII (OR = 0.927; 95% CI = 0.888–0.967), as indicated in Table 5.

Table 5 Logistic regression findings for evaluating the relationship between the DII score and osteoporosis
Full size table

Table 6 shows the relationship between DII tertiles and osteoporosis in both crude and adjusted models. According to the crude model, there was no significant correlation between people in the first and third tertiles of DII scores (crude OR = 1.35, 95% CI: 0.82–2.21, P = 0.240), but the odds of osteoporosis for women in the second tertile of DII scores were approximately 2.22 times higher than those in the third tertile (crude OR = 2.22, 95% CI: 1.35–3.67, P = 0.002). Furthermore, although there was no significant correlation between individuals in the first and third tertiles of DII scores (adjusted OR = 1.47, 95% CI: 0.87–2.49, P = 0.154), the adjusted model showed that the probabilities of osteoporosis for those in the second tertile were almost 2.32 times higher than those in the third (adjusted OR = 2.32, 95% CI: 1.36–3.97, P = 0.002).

Table 6 Logistic regression results for assessing the association between the DII tertiles and risk of osteoporosis
Full size table

Discussion

According to the results of the present research, maintaining a diet with a high insulin index may improve bone mass density. In this work, we examined the relationship between the risk of osteoporosis and the dietary insulin score. Due to its reliance on insulin response rather than other factors, DII is a more accurate predictor of the probability of acquiring chronic disease compared to GI [18]. Previous studies indicated a somewhat advantageous correlation between DII and the probability of IR [13]. Our results demonstrate that the controls follow a diet with a higher tendency for insulinemia, as seen by the significantly higher mean DII in this group. This postprandial hyperinsulinemia may occur in IR gradually [9]. Furthermore, past studies have shown a positive association between IR and BMD [8, 10, 11]. Bazic et al. [10] examined the correlation between IR and BMD in postmenopausal women from Serbia. A positive connection was found between HOMA-IR and BMD-spine as well as bone mineral content. Furthermore, IR positively affects volumetric bone mineral density, as shown by a cross-sectional study [11] on postmenopausal Caucasian women. Additionally, the results of a cohort research [8], including close to 6000 senior men and women demonstrated a significant correlation between increased insulin and blood glucose levels and increased bone density and a decreased fracture risk. The relationship between IR and BMD is rather intricate. As a result, the processes that determine their precise relationship are not well known. Insulin may have anabolic effects on bone based on the research that is currently available [7]. The insulin receptors are expressed on the surface of osteoblasts and osteoclasts alike. Insulin is an anabolic hormone that has been shown in experimental investigations to support osteoblast growth [30]. Moreover, insulin-like growth factor 1 (IGF-1) receptors that are found on osteoblasts may be the source of this. Consequently, an increase in BMD may result from bone formation [31]. The inverse association between sex hormone-binding globulin (SHBG) and insulin is another likely example of a greater BMD in those with higher insulin levels [32]. Then, a drop in SHBG levels causes a rise in free estrogen concentration, which raises BMD [33].

In addition, prior research has shown the anabolic function of insulin in bone and established that physiological insulin concentrations may stimulate osteoblast growth, glucose uptake, and inhibit osteoclast activity [34]. Based on our research, the control group’s mean intake of protein and carbohydrates is significantly higher than that of the case group, which may increase the amount of insulin secreted in their bodies [35]. Additionally, because insulin plays an anabolic role in bone [34], it is possible that this group’s bone mass may be higher than that of the case group. Furthermore, the control group consumed significantly more vitamins and provitamins (A, alpha-carotene, beta-cryptoxanthin, and C), as well as minerals (calcium, phosphorus, magnesium, zinc, manganese, and selenium), than the case group, which consumed more total fat, saturated fat, MUFA, PUFA, and vitamin E. Furthermore, an increase in reactive oxygen species (ROS) levels might produce oxidative stress [36]. Previous research suggests that oxidative stress may contribute to osteoporosis because of its function in chronic inflammation [37]. Antioxidant micronutrients, such as vitamin A, vitamin C, zinc, manganese, and selenium help the body’s defense system against ROS [38] and reduce oxidative stress [36], which contributes to bone health. In addition, the synthesis of collagen and other proteins that comprise the bone structure is influenced by micronutrients, including calcium, magnesium, zinc, and vitamin C [39]. They contribute to the normal development and maintenance of bone mass by performing catalytic functions in the synthesis of bone matrix.

Obesity is exacerbated by an excessive consumption of dietary lipids, such as total fat, polyunsaturated fatty acids, and saturated fatty acids. This, in turn, results in the production of white adipose tissue, which generates pro-inflammatory substances, thereby establishing a protracted inflammatory state [40]. Osteoclasts are known to be primarily activated by pro-inflammatory cytokines like interleukin (IL)-1 and tumor necrosis factor-α (TNF-α), although IL-6 functions in concert with other agents that degrade bone [41]. Therefore, a number of variables that are linked to excess body fat, such as an increase in inflammatory cytokines and a shift in the pattern of adipokine production, negatively impact bone structure [42]. Based on our research, individuals in the lowest third of DII had notably greater consumption of calories, total fat, saturated fat, monounsaturated fat (MUFA), polyunsaturated fat (PUFA), vitamin E, and vitamin D. On the other hand, participants in the highest third of DII had considerably higher intakes of beta-cryptoxanthin, manganese, and selenium. No significant differences were observed among the tertiles for other nutrients. Consequently, the aforementioned mechanisms suggest that a higher ingestion of antioxidants, including beta-cryptoxanthin and manganese, may have a protective effect on bone health in participants who were in the third tertile of DII.

The relationship between DII and the risk of osteoporosis has not been investigated in previous research, to the best of our knowledge. On the other hand, additional research has demonstrated a robust correlation between specific indices and conditions, such as IR [13] and metabolic syndrome [43]. The current investigation also has the advantage of employing a validated food frequency questionnaire to gather data on food consumption. Nevertheless, it is subject to specific limitations. The initial limitation is that insulin scores can only provide an estimate of the total amount of insulinogenic food consumed. They are unable to evaluate the frequency of meals or the composition of food, which could potentially affect insulin response. The second reason is that the case-control design of this study precludes the examination of a coincidental relationship between the risk of osteoporosis and the dietary insulin index. Consequently, it is imperative that additional research corroborates these findings. Two additional drawbacks are recall and choose bias, which are inherent limitations of case-control studies. In order to mitigate recollection bias, this investigation implemented a validated FFQ. Additionally, we examined the FII of dietary items using the FII data from other research [14, 27, 28]. The comparable insulin index of foods must be used for the FII of some foods that were not included in the FFQ because Iranian foods have not yet been measured for FII. This could lead to a slight difference between the FII of foods consumed by individuals and the actual amount of this index in the reference list. Therefore, our findings are impacted by their limits. Another possible drawback of this research might be the absence of information on the length of time spent in the sun, since this could have an impact on bone health. Finally, we only considered the short-term consequence of ingesting foods that increase insulin index, which may increase the need for postprandial insulin and have transitory effects on serum insulin levels [44]. However, an insulinogenic diet may not have a long-term effect on insulin levels. Consequently, it is recommended that additional research be conducted to ascertain the long-term effects of the diet’s propensity to induce insulinemia on serum insulin and bone mass.

Conclusions

In summary, our results suggested that postmenopausal women may experience an increase in bone mass density by adhering to a diet that is abundant in foods with a higher insulin index. Consequently, it may be essential to consume nutrients that stimulate insulin production in order to prevent osteoporosis. Additional research, particularly prospective cohort studies, is necessary to substantiate these conclusions.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

DII:

Dietary insulin index

FFQ:

Food frequency questionnaire

IR:

Insulin resistance

HOMA-IR:

Homeostasis model assessment of IR

BMD:

Bone mineral density

FII:

Food insulin index

GI:

Glycemic index

IGF-1:

Insulin-like growth factor 1

SHBG:

Sex hormone-binding globulin

vBMD:

Volumetric bone mineral density

Kg:

Kilogram

Cm:

Centimeter

BMI:

Body mass index

m:

Meter

mg:

Milligram

g:

Gram

µg:

Microgram

OR:

Odds ratio

SD:

Standard deviation

CI:

Confidence interval

MET:

Metabolic equivalent

kg/m2 :

Kilogram per square meter

OCP:

Oral contraceptive pill

Kcal:

Kilo calorie

MUFA:

Monounsaturated fatty acid

PUFA:

Polyunsaturated fatty acid

RAE:

Retinol activity equivalent

ROS:

Reactive oxygen species

IL:

Interleukin

TNF-α:

Tumor necrosis factor-α

References

  1. Soltani A, Pazhouhi M, Bastan HM, Mirfeizi S, Dashti R, Hosseinnezhad A. Bone mineral density variations in 20–69 year. Popul Tehran/Iran ISMJ. 2002;5(1):41–9.

    Google Scholar 

  2. Caligiuri P, Giger ML, Favus MJ, Jia H, Doi K, Dixon LB. Computerized radiographic analysis of osteoporosis: preliminary evaluation. Radiology. 1993;186(2):471–4.

    Article  CAS  PubMed  Google Scholar 

  3. Curtis EM, van der Velde R, Moon RJ, van den Bergh JP, Geusens P, de Vries F, et al. Epidemiology of fractures in the United Kingdom 1988–2012: variation with age, sex, geography, ethnicity and socioeconomic status. Bone. 2016;87:19–26.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Pajouhi M, Komeilian Z, Sedaghat M, Jalili R, Soltani A, Larijani B. Effect of education by booklet on knowledge and practice of osteoporotic patients. Payesh J. 2003;3(1):67–74.

    Google Scholar 

  5. Hernandez-Rauda R, Martinez-Garcia S. Osteoporosis-related life habits and knowledge about osteoporosis among women in El Salvador: a cross-sectional study. BMC Musculoskelet Disord. 2004;5(1):29.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Keramat A, Patwardhan B, Larijani B, Chopra A, Mithal A, Chakravarty D, et al. The assessment of osteoporosis risk factors in Iranian women compared with Indian women. BMC Musculoskelet Disord. 2008;9(1):1–10.

    Article  Google Scholar 

  7. Tonks KT, White CP, Center JR, Samocha-Bonet D, Greenfield JR. Bone turnover is suppressed in insulin resistance, independent of adiposity. J Clin Endocrinol Metabolism. 2017;102(4):1112–21.

    Article  Google Scholar 

  8. Stolk R, Van Daele P, Pols H, Burger H, Hofman A, Birkenhäger J, et al. Hyperinsulinemia and bone mineral density in an elderly population: the Rotterdam Study. Bone. 1996;18(6):545–9.

    Article  CAS  PubMed  Google Scholar 

  9. Choubey A, Girdhar K, Kar AK, Kushwaha S, Yadav MK, Ghosh D, et al. Low-dose naltrexone rescues inflammation and insulin resistance associated with hyperinsulinemia. J Biol Chem. 2020;295(48):16359–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bazić-Sretenović D, Veselinovć M, Čekerevac I, Nikolić-Turnić T, Azanjac A, Korićanac A, et al. The relationship between insulin resistance, bone mineral density and fracture risk in postmenopausal women. Vojnosanit Pregl. 2022;79(7):658–65.

    Article  Google Scholar 

  11. Shanbhogue VV, Finkelstein JS, Bouxsein ML, Yu EW. Association between insulin resistance and bone structure in nondiabetic postmenopausal women. J Clin Endocrinol Metabolism. 2016;101(8):3114–22.

    Article  CAS  Google Scholar 

  12. Kalimeri M, Leek F, Wang NX, Koh HR, Roy NC, Cameron-Smith D, et al. Association of insulin resistance with bone strength and bone turnover in menopausal chinese-singaporean women without diabetes. Int J Environ Res Public Health. 2018;15(5):889.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mirmiran P, Esfandiari S, Bahadoran Z, Tohidi M, Azizi F. Dietary insulin load and insulin index are associated with the risk of insulin resistance: a prospective approach in tehran lipid and glucose study. J Diabetes Metabolic Disorders. 2015;15(1):23.

    Article  Google Scholar 

  14. 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  PubMed  Google Scholar 

  15. Anjom-Shoae J, Shayanfar M, Mohammad-Shirazi M, Sadeghi O, Sharifi G, Siassi F, et al. Dietary insulin index and insulin load in relation to glioma: findings from a case–control study. Nutr Neurosci. 2021;24(5):354–62.

    Article  PubMed  Google Scholar 

  16. Bao J, De Jong V, Atkinson F, Petocz P, Brand-Miller JC. Food insulin index: physiologic basis for predicting insulin demand evoked by composite meals. Am J Clin Nutr. 2009;90(4):986–92.

    Article  CAS  PubMed  Google Scholar 

  17. De Araújo I, Parreiras-e-Silva L, Carvalho A, Elias J, Salmon C, de Paula F. Insulin resistance negatively affects bone quality not quantity: the relationship between bone and adipose tissue. Osteoporos Int. 2020;31(6):1125–33.

    Article  PubMed  Google Scholar 

  18. Sung KC, Lee MY, Kim YH, Huh JH, Kim JY, Wild SH, et al. Obesity and incidence of diabetes: Effect of absence of metabolic syndrome, insulin resistance, inflammation and fatty liver. Atherosclerosis. 2018;275:50–7.

    Article  CAS  PubMed  Google Scholar 

  19. Karamati M, Yousefian-Sanni M, Shariati-Bafghi S-E, Rashidkhani B. Major nutrient patterns and bone mineral density among postmenopausal Iranian women. Calcif Tissue Int. 2014;94(6):648–58.

    Article  CAS  PubMed  Google Scholar 

  20. Goldberg G, Black A, Jebb S, Cole T, Murgatroyd P, Coward W, et al. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr. 1991;45(12):569–81.

    CAS  PubMed  Google Scholar 

  21. Aadahl M, Jørgensen T. Validation of a new self-report instrument for measuring physical activity. Med Sci Sports Exerc. 2003;35(7):1196–202.

    Article  PubMed  Google Scholar 

  22. Klishadi R, Khosravi A, Famouri F, Sadeghi M, Shirani S. Assessment of physical activity of adolescents in Isfahan. J Shahrekord Univ Med Sci. 2001;3(2):27–33.

    Google Scholar 

  23. Wareham NJ, Jakes RW, Rennie KL, Schuit J, Mitchell J, Hennings S, et al. Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European prospective investigation into Cancer and Nutrition (EPIC) study. Public Health Nutr. 2003;6(4):407–13.

    Article  PubMed  Google Scholar 

  24. 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  PubMed  Google Scholar 

  25. Keshteli AH, Esmaillzadeh A, Rajaie S, Askari G, Feinle-Bisset C, Adibi P. A dish-based semi-quantitative food frequency questionnaire for assessment of dietary intakes in epidemiologic studies in Iran: design and development. Int J Prev Med. 2014;5(1):29.

    PubMed  PubMed Central  Google Scholar 

  26. Ghaffarpour M, Houshiar-Rad A, Kianfar H. The manual for household measures, cooking yields factors and edible portion of foods. Tehran: Nashre Olume Keshavarzy. 1999;7:213.

    Google Scholar 

  27. 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  PubMed  Google Scholar 

  28. 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  PubMed  PubMed Central  Google Scholar 

  29. Nimptsch K, Brand-Miller JC, Franz M, Sampson L, Willett WC, Giovannucci E. Dietary insulin index and insulin load in relation to biomarkers of glycemic control, plasma lipids, and inflammation markers. Am J Clin Nutr. 2011;94(1):182–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fulzele K, Riddle RC, DiGirolamo DJ, Cao X, Wan C, Chen D, et al. Insulin receptor signaling in osteoblasts regulates postnatal bone acquisition and body composition. Cell. 2010;142(2):309–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Schmid C. The regulation of osteoblast function by hormones and cytokines with special reference to insulin-like growth factors and their binding proteins. J Intern Med. 1993;234(6):535–42.

    Article  CAS  PubMed  Google Scholar 

  32. Khaw KT, Barrett-Connor E. Fasting plasma glucose levels and endogenous androgens in non-diabetic postmenopausal women. Clin Sci (Lond). 1991;80(3):199–203.

    Article  CAS  PubMed  Google Scholar 

  33. Birkeland KI, Hanssen KF, Torjesen PA, Vaaler S. Level of sex hormone-binding globulin is positively correlated with insulin sensitivity in men with type 2 diabetes. J Clin Endocrinol Metab. 1993;76(2):275–8.

    CAS  PubMed  Google Scholar 

  34. Thrailkill KM, Lumpkin CK Jr, Bunn RC, Kemp SF, Fowlkes JL. Is insulin an anabolic agent in bone? Dissecting the diabetic bone for clues. Am J physiology-endocrinology Metabolism. 2005;289(5):E735–45.

    Article  CAS  Google Scholar 

  35. Newsholme P, Krause M. Nutritional regulation of insulin secretion: implications for diabetes. Clin Biochem Rev. 2012;33(2):35–47.

    PubMed  PubMed Central  Google Scholar 

  36. Banfi G, Iorio EL, Corsi MM. Oxidative stress, free radicals and bone remodeling. Clin Chem Lab Med (CCLM). 2008;46(11):1550–5.

    Article  CAS  PubMed  Google Scholar 

  37. Hofbauer L, Brueck C, Shanahan C, Schoppet M, Dobnig H. Vascular calcification and osteoporosis—from clinical observation towards molecular understanding. Osteoporos Int. 2007;18(3):251–9.

    Article  CAS  PubMed  Google Scholar 

  38. Gutteridge JM. Biological origin of free radicals, and mechanisms of antioxidant protection. Chem Biol Interact. 1994;91(2–3):133–40.

    Article  CAS  PubMed  Google Scholar 

  39. Gulekli B, Davies MC, Jacobs HS. Effect of treatment on established osteoporosis in young women with amenorrhoea. Clin Endocrinol (Oxf). 1994;41(3):275–81.

    Article  CAS  PubMed  Google Scholar 

  40. Muñoz A, Costa M. Nutritionally mediated oxidative stress and inflammation. Oxid Med Cell Longev. 2013;2013.

  41. Schett G. Effects of inflammatory and anti-inflammatory cytokines on the bone. Eur J Clin Invest. 2011;41(12):1361–6.

    Article  CAS  PubMed  Google Scholar 

  42. Verroken C, Zmierczak H-G, Goemaere S, Kaufman J-M, Lapauw B. Insulin resistance is associated with smaller cortical bone size in nondiabetic men at the age of peak bone mass. J Clin Endocrinol Metabolism. 2017;102(6):1807–15.

    Article  Google Scholar 

  43. 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–e864.

    Article  PubMed  Google Scholar 

  44. Giovannucci E, Michaud D. The role of obesity and related metabolic disturbances in cancers of the colon, prostate, and pancreas. Gastroenterology. 2007;132(6):2208–25.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

The authors reported there is no funding associated with the work featured in this article.

Author information

Authors and Affiliations

  1. Department of Nutrition, Electronic Health and Statistics Surveillance Research center, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Shakiba Solgi

  2. Proteomics Research Center, Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Farid Zayeri

  3. Department of Nutrition, Electronic Health and Statistics Surveillance Research center, Science and Research Branch, Islamic Azad University, Hesarak Blvd, Daneshgah Square, Sattari Expressway, Tehran, Iran

    Behnood Abbasi

Authors
  1. Shakiba Solgi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farid Zayeri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Behnood Abbasi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Sh.S; was involved in writing and original draft preparation. F.Z; participated in data analysis. B.A; contributed to conceptualization, methodology, supervision, writing, review and editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Behnood Abbasi.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the ethics committee of the Islamic Azad University, Science and Research Branch of Tehran, Iran (IR.IAU.SRB.REC.1396.119). All participants provided written informed consent. All study methods were carried out in accordance with relevant guidelines and regulations.

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

Solgi, S., Zayeri, F. & Abbasi, B. Association between dietary insulin index and postmenopausal osteoporosis in Iranian women: a case-control study. BMC Women's Health 24, 401 (2024). https://doi.org/10.1186/s12905-024-03248-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12905-024-03248-z

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us