Anthocyanidins

RAW264.7 cells, a mouse macrophage cell line, were used as osteoclast precursor cells and maintained in α modified essential medium (α-MEM) supplemented with 10% fetal bovine serum (FBS) at 37°C and 5% CO₂. For osteoclast induction, cells were plated in a 96-well plate at a density of 4×10³ cells/well and stimulated with 100 ng/ml RANKL for 4 days. For the inhibition study, cells were pre-incubated in α-MEM supplemented with vehicle or with various concentrations of anthocyanin-rich extracts and anthocyanidins, 1 h before the addition of RANKL. To confirm multinucleated osteoclast formation, the cultured cells were fixed in 10% formalin for 3 minutes, and then stained with an osteoclast marker enzyme, tartrate-resistant acid phosphatase (TRAP). Effects of anthocyanins and anthocyanidins on osteoclast formation were evaluated by morphological observations and the intensity of TRAP staining was measured at 520 nm using a spectrophotometer (SpectraMax M5; Molecular Devices, Sunnyvale, CA, USA).
Osteoblasts were isolated from newborn calvariae of C57BL/6J mice, as described previously with slight modifications [19] (link). Briefly, calvariae were minced and sequentially digested with collagenase solution at 37°C. Cells retrieved from the osteogenic cell fractions were separately cultured in α-MEM supplemented with 10% FBS and antibiotics. After 24 h, cells were pooled and grown in multi-well plates in the same medium containing 50 µg/ml of ascorbic acid (AA), 10 µM dexamethasone (Dex) and 10 mM β-glycerophosphate (β-GP) with or without anthocyanin-rich extracts. After two weeks culture, cells were stained with von Kossa’ s staining to determine the matrix mineralization, as described previously [19] (link).

DII was calculated from the food questionnaire using formulas developed in 2009, and adjusted in 2014 and 2019. These formulas are calculated using 11 data sets from around the world [Australia (National Nutrition Survey), Bahrain (National Nutrition Survey for Adult Bahrainis), Denmark (Danish National Survey of Diet and Physical Activity), India (Indian Health Study), Japan (National Nutrition Survey Report), Mexico (Mexican National Health and Nutrition Survey), New Zealand (National Nutrition Survey), South Korea (Korean NHANES), Taiwan (Nutrition and Health Survey in Taiwan), the United Kingdom (National Diet and Nutrition Survey), and the United States (NHANES)] that formed the basis for calculated DII mean and standard deviation (SD) values for 45 food parameters [30 (link),31 (link)]. Among them, 9 parameters were pro-inflammatory (energy, proteins, total fats, carbohydrates, saturated fatty acids, trans fatty acids, cholesterol, iron, and vitamin B₁₂) and the other 36 had anti-inflammatory properties (monounsaturated fatty acids, polyunsaturated fatty acids, ω-3 fatty acids, ω-6 fatty acids, dietary fiber, alcohol, vitamins A, D, E, C, and B6, β-carotene, thiamin, riboflavin, niacin, folate, Mg, Se, Zn, flavan-3-ols, flavones, flavonols, flavonones, anthocyanidins, isoflavones, caffeine, garlic, onion, pepper, oregano, rosemary, eugenol, saffron, ginger, and turmeric).
In detail, nutritional components for the calculation of inflammatory dietary indices were taken from databases containing the chemical composition of food and beverages. Depending on the method of preparation, the values for flavan-3-ols, flavones, flavonols, flavonones, anthocyanidins, and isoflavones in raw foods were multiplied by retention factors. The retention factor for cooking is 0.59, for frying 0.5, and for baking 1.09 [33 (link)]. For each food component, the specific inflammatory index of the individual component was first calculated in such a way that the global average intake for the calculated food component was subtracted from the obtained average value of the intake of the food component, and the thus obtained value was divided by the standard deviation of the calculated individual food component, which resulted in a specific z-value. The obtained z-value of the food component was converted into percentiles centered at zero and doubled. A value of one was subtracted from the thus obtained value, which was ultimately multiplied by the value of the pro-inflammatory or anti-inflammatory effect of the calculated component. The inflammatory index of the diet was obtained by adding up all 45 inflammatory indices of individual components. The ranges of the obtained values must be between 7.98, which represents the maximum pro-inflammatory effect, and −8.87, which represents the maximum anti-inflammatory effect of the diet. Values for the global average intake, standard deviations, and pro-inflammatory/anti-inflammatory effects of all 45 food components as well as the method of calculating the DII were taken from the papers of Shivappa et al. (2014) and Hebert et al. (2019) [30 (link),31 (link)].

The DII is an algorithm developed to categorize various diets according to their inflammatory potential. A modified and updated version of DII calculation designed by Shivappa et al. [18 (link)] was used in this study. A detailed description of the updated DII has been previously described [18 (link)]. DII was compiled based on an extensive review of the literature from 1950 to 2010. In total, DII authors reviewed 1943 articles with 45 selected food parameters. Authors of DII evaluated the association of dietary components with 6 inflammatory biomarkers (IL-1β, IL-4, IL-6, IL-10, TNF-α and CRP). The inflammatory potential for each food parameter was scored according to whether it increased (+1), decreased (−1), or had no effect on 6 inflammatory biomarkers. Authors of DII calculated the global daily average intake of each dietary food product, along with the standard deviation, based on 11 data sets from around the world (USA, Australia, the Kingdom of Bahrain, Denmark, India, Japan, New Zealand, Taiwan, South Korea, Mexico, and United Kingdom). Dietary intake of the DII components was compared to the standard global as a Z-score, which was achieved by subtracting the standard mean from the amount reported and dividing this value by its standard deviation [18 (link)]. Then, this value was converted to a centered percentile score. To achieve a symmetrical distribution with values centered on 0 (null) and bounded between −1 (maximally anti-inflammatory) and +1 (maximally pro-inflammatory), each percentile score was doubled and then ‘1’ was subtracted. The centered percentile values were then multiplied by the overall pro- and anti-inflammatory effect score for each dietary component. Finally, all results were summed. Higher scores indicated that the diet was more pro-inflammatory, and lower DII scores represented a more anti-inflammatory diet. Results ranged from 7.98 (maximally pro-inflammatory) to −8.87 (maximally anti-inflammatory) [18 (link)]. Thirty-seven dietary food components and products were used to calculate the DII score, including: 29 anti-inflammatory elements: monounsaturated fatty acids (MUFAs), PUFAs, n-3 fatty acids, n-6 fatty acids, fiber, alcohol, vitamins A, D, E, C, and B₆, β-carotene, thiamine, riboflavin, niacin, folic acid, magnesium, selenium, zinc, flavan-3-ol, flavones, flavonols, flavonones, anthocyanidins, isoflavones, caffeine, garlic, onion, green/black tea, and 8 pro-inflammatory elements: carbohydrates, protein, total fat, SFAs, trans fat, cholesterol, iron, and vitamin B₁₂. Energy-adjusted values (the nutrient density method) were used to decrease the influence of different energy intakes among study participants [35 (link)].