Overweight/obesity is characterized by excessive body weight due to the accumulation of fatty tissue with negative effects on the health. It represents the sixth most important risk factor contributing to the overall burden of disease worldwide (1). For the World Health Organization (WHO), obesity is a complex and multi-factorial pathology, which involves environmental, social, cultural, genetic, physiological, metabolic, psychological and comportamental factors (2). In fact, the obese suffer from social bias, prejudice and discrimination, and different comorbidities including dyslipidaemia, coronary heart disease, hypertension and stroke, cancer, diabetes mellitus, osteoarthritis, and pulmonary diseases (2). In Italy, in 2015, more than a third of the adult population (35,3%) is overweight, while one in ten people is overweight (9,8%); overall, 45,1% of individuals aged 18 or over is overweight even if the analysis of the trend shows a decline of childhood overweight and obesity between 2008-2016 (3).The most used indicator in the clinical evaluation and in the classification of overweight and obesity is the BMI ( Body Mass Index ) and it is the numerical value obtained from the ratio between the weight in Kg and the height expressed in meters squared (normal weight, BMI 18.5-24.99; overweight, 25-29,99; obesity class I, 30-34,9; obesity class II, 35-39,9; obesity class III, ≥40). Although BMI is a more accurate measurement method of total fats than simple body weight, it has some limits. In fact, with BMI measurement total body fat is overestimated in subjects with highly represented muscle mass and is underestimated in subjects affected by muscle loss (4). Moreover, BMI underestimates health risk for obese patients (5). The limit of BMI to indicate obesity in postmenopausal women does not seem to be appropriate. In fact, a study carried out on 1329 postmenopausal women and in which fat mass was measured by Dual-Energy X-Ray absorptiometry (DEXA) has indicated that for this category of subjects the limit of BMI should be 24,9 to define the obesity state (6). On the other hand, with advancing age women tend to lose bone mass and muscle mass and to increase visceral fat and this, together with a decrease in energy expenditure, can explain the higher risk of hypertension, lipid changes, diabetes and cardiovascular diseases that occur in postmenopausal women than in premenopausal women (7).
Cell membrane represents the fundamental structure of the cells in living organisms. They are constituted by proteins, glicoproteins, and lipids of which the main ones are phospholipids. The last are formed by a glycerol molecule with two fatty acid molecules and a phosphate group linked to different bases as choline, ethanolamine, serine, and inositol. Due to the structure, phospholipids have an amphipathic behaviour with the polar heads facing extracellular and intracellular aqueous environment, and the hydrophobic tails facing each other to form the thickness of the membrane.
Fatty acids (FAs) are divided into saturated and unsaturated fatty acids (SFA, USFA). SFA have the typical linear and rigid structure and USFA have a folded and slightly fluid structure. The most represented FA in mammalian membranes is palmitic acid (C16:0) whose chain can be stretched by specific elongases. The family of elongases includes elongases 1,3 e 6 that preferentially elongate saturated and monounsaturated fatty acids, whereas elongases 2,4 and 5 that elongate polyunsaturated fatty acids. SAT and monounsaturated fatty acids (MUFA) are also substrates for desaturates that are responsible for the polyunsaturated fatty acids (PUFA) synthesis. Moreover, FAs are divided into four classes based on the length of the monocarboxylic chain: short-chain FAs with 4 carbon atoms, medium-chain FAs with 6-12 carbon atoms, long-chain FA with 14-18 carbon atoms, and very long-chain FAs with 20-36 carbon atoms (8). As the affinity of SFA for desaturase enzymes is very high, long chain SFA cannot be formed in high amounts, and their increase can become an indicator of dysmetabolic lipid pathways (9).
Taken together, the studies above reported indicate the change of lipid tissue in menopause and the characteristic of FAs in cell membrane, but their relationship is not completely clarified. Therefore, we aimed to investigate : 1) the relation between FA composition of cell membrane and menopause in relation to the BMI; 2) the change of cell membrane FAs in the first 5 years and over 5 years after menopause.
Materials and Methods
37 menopausal women without cardiovascular, ipertensive, thyroid, diabete, and cancer disease were included in the present study authorized from the Local Health Unit Company Umbria (Cod. PG263274). All patients signed the informed consent. A specific questionnaire was carried out with the patients to find out: BMI, pregnancies, menarche age, pregnancies, physical activity, smoker, age of onset of menopause, years since onset of menopause. BMI was assessed according to the World Health Organization criteria and the study above reported (6).
Blood samples were collected between October 1, 2018, and February 28, 2019 by using standardized procedures.
Erythrocyte fatty acid profile analysis
The blood sample was taken by puncturing and dripping blood from the finger until collecting 0,5 ml in vacutainer tubes containing ethylenediaminetetraacetic acid (EDTA) in the fasting state. Samples were stored at 4 °C until analysis. For mature erythrocyte selection, blood samples were centrifuged and entered a specific automatized procedure in which cell fraction was isolated on the basis of high density of the aged cells (9) The robotics performs all the subsequent steps for the cell lysis, isolation of the membrane pellets, phospholipid extraction from pellets and treatments (9). Erythrocyte cell membrane lipid analysis was performed by Fatpharmacy test that permits a lipidomic analysis with molecular check up of membrane lipids. The analysis includes capillary column gas chromatography (GC). Gas chromatography is the gold standard method for the determination of fatty acids, and it is performed under optimal separation condition, to identify a group of 10 cis fatty acids and 2 trans fatty acids (called lipidomic cluster). This cluster is always present in biological membranes because the fatty acids that make it up are part of the fundamental structure of the hydrophobic layer of the membrane. The lipidomic cluster of healthy subjects and the optimal intervals, to be used as a reference for the object of study samples, were obtained from an examination of the literature together with the study of the values obtained by Italian subjects present in the Lipinutragen database (10). The amount of each FA was calculated as a percentage of the total FA content (relative %), being > 97% of the GC peaks recognized with appropriate standards (9).
We consider two samples
For i = 1,2,..., r and j = 1, 2, let yi,j be the mean of the data concerning the concentration of the acid Xt under the treatment
Moreover, let .μi, bj e the mean corresponding to the world menopause women populations associated with, the conentration of the acid X under the treatment and put
LeX Sj be the covariance matrix associated with j = 1,2, and define a pooled-sample covariance matrix by S
We compute S by means of the Excel functions COVAR or COVARIANZA. Since these functions deal with population covariance rather than sample covarianc, in (1) we take the product n1S1 + n2S2 rather than the product (n1–1)S1 + (n2 – 1)S2 in ([1, §6.4 (3)]). We test the null hypothesis μ1= μ2 (H0), without specifying the common value, versus the alternative hypothesis μ1≠μ2 (H1). Note that, in general, it would not be advisable to test H0 by taking each acid separately (see also [1, Exercise 6.4.7). In our samples n1=22, n2=15, r=12, and hence n1+n2 > r+1. This condition guarantees that the matrix S is non-singular, and so the matrix S–1 is well-defined (see also ).
We consider the statistic ϕ defined by
Without loss of generality, we can assume that the total world population of menopause women has a normal distribution, and that the samples and are independent. The studied corresponding test statistic is
which has a Fisher-type distribution with r and n1+n2–r–1 degrees of freedom (see also Theorem 6.4.1) (11). We compute the value of the statistics ϕ in (2). The Excel functions FDIST and DISTRIB.F compute the p-value related to F and ϕ, that is the quantity
where the symbol in (3) denotes the probability that the test statistic F is greater than or equal to ϕ, when the null hypothesis H0 is satisfied. The p-value is the smallest significance level at which the obtained data lead to reject the null hypothesis. As we consider the significance levels α1 = 0.05 or α2 = 0.01, the null hypothesis is rejected if and only if the p-value is less than or equal to α1 (or α2). Thus, a p-value p < α1 (or p < α2) indicates that the sample
The analysis of BMI revealed that of 37 menopausal women object of sutdy, 15 had a BMI value <25 and 22 >25 (Tab.1). According to the World Health Organization criteria and the study showing the value of reference of BMI in menopause (6), the control samples were considered women with BMI <25 and case samples women with BMI >25. The media ±SD of BMI showed significant differences between the two groups (*p<0.001). There were no significant differences in age. Also percentage values of smokers, physical activity, and pregnancies were similar.
Differences in population characteristic between women with BMI<25 (control samples) and BMI>25 (case samples). *p<0.001 against control group (BMI<25)
|Patients n° (%)||15 (41)||22 (59)|
|Age years (media±SD)||57±10.8||59±7.88|
|physical activity (%)||33||31.8|
We then analysed the time from menopause and divided samples between those who were within and those who were over 5 years after menopause (Fig.1). 60% (n°9) of control samples were within 5 years and 40% (n°6) over 5 years (Fig.1a); 45% (n°10) of control samples were within 5 years and 55% (n°12) over 5 years (Fig.1b)
We then analyzed the percentage of the population with an age of appearance of the menarche greater than 13 years and the onset of menopause at the age less than 38 years (Fig.2). Interestingly, the results showed that among women with menarche age >13 years, 75% were case samples (Fig.2a) and among women with the age of onset the menopause <38, 83% were case samples (Fig.2b).
Erythrocyte membrane fatty acid profile
SFA, MUFA, PUFA, and n-6 PUFA/n-3 PUFA levels in erytrocyte membrane were considered (Table 2). The results showed that the percentage of women who had physiological levels of SFA was lower in case samples than control samples, and percentage of physiological levels MUFA and PUFA was higher. All samples that did not have physiological levels, they always had higher values and never lower. Therefore, erythrocyte membrane of woman with BMI>25 were enriched in SFA and poorer in MUFA and PUFA than woman with BMI<25. Physiological level of n-6/n-3 PUFA ratio was lower in woman with BMI>25 than in woman with BMI<25, by indicating a high level of n-6 PUFA (Table 2).
Differences in percent of patients that had physiological level of SFA, MUFA, PUFA, n-6/n-3 PUFA between control group (BMI<25) and case group (BMI>25).
|range||% patients||% patients|
Then, we analyzed the percentages of patients who had physiological values by dividing them between women who were within 5 years of menopause and those who were over 5 years of menopause (Table 3). As you can see, the percentages of physiological values increased over time, with the exception of the SFA of women belonging to the control group, indicating an improvement of the parameters over time regardless of the BMI.
Differences in percent of patients that had physiological level of SFA, MUFA, PUFA, n-6/n-3 PUFA between case group and control group.
|range||%<5 years||%>5years||%<5 years||%>5years|
However, if you consider the absolute values of SFA, MUFA, PUFA there are no significant variations between the case and the group control both in reference to the total of women and between women who are within 5 years of menopause and those who are beyond 5 years after menopause
Notably, the value of n-6/n-3PUFA ratio of control group, that was within the physiological range (Table 3), was similar if you consider the time within 5 years and beyond 5 years after menopause (Fig.4a). Differently, medium values (5.5-7 range) was reached by woman who were in menopause within 5 years and high value (>7) by woman who were in menopause beyond 5 years (Fig.4a). As above reported, no woman with BMI >25 who were in menopause within 5 years had physiological values of n-6/n-3PUFA ratio but this was achieved after 5 years, indicating a possible normalization over time (Table 3, Fig. 4b). Medium and high values were similar within and beyond 5 years after menopause (Fig.4b)
Metabolically, the composition of isolated membrane from cells is the result of biosynthetic capacities that are put in place thanks to the presence of many cofactors for the enzymatic activities. The mature erythrocyte cannot biosynthesize lipids, so its membrane also depends on the exchanges it makes in vivo with the lipoproteins and with the tissues (12). In addition, the erythrocyte's membrane is composed of all the families of FAs. By considering that the average life of the erythrocyte is 120 days, the analysis in these cells provides a fairly stable picture of what happens in cell membranes. Moreover, the study was performed in erythrocytes because they are ideal cells for functional lipidomic analysis. In this study, we showed that BMI does not influence the composition of total SFA, MUFA and PUFA in cellular membrane but we identified BMI as a critical factor for the normalization of n-6/n-3PUFA ratio beyond 5 years after menopause.
The n-6 PUFA are formed thank to delta-6 desaturase that can be influenced by different cofactors such as Fe, Zn, Mg, and B2, B3, and B6 vitamins (13). Dihomo gamma-linolenic acid (DGLA regulates the n-6 PUFA metabolism, including prostaglandins, thromboxanes, and leukotrienes, molecules essential for anti-inflammatory and coagulative processes (14). Moreover, DGLA is the substrate for the enzyme delta-5 desaturase responsible for arachidonic acid (AA) synthesis (15). AA is the precursor of prostanoids, leukotrienes, and lipoxins, involved in inflammation process (16). The enzyme delta-5 desaturase is regulated by the presence of insulin and cortisol (17).
The omega-3 pathway starts by the action of enzyme delta-6 desaturase (18) with subsequent steps of elongation and desaturation, leading to the synthesis of eicosapentaenoic acid (EPA), and other types of prostanoids and leukotrienes (19). Elongation and desaturation of EPA gives docosahexaenoic acid (DHA) synthesis (19). The role of EPA and DHA is not only to balance the inflammatory effects of AA, but they play several other roles, producing neuroprotectins (from DHA) and resolvins (from EPA), which provide specific protective activity at picomolar concentration in tissues (12). It has been reported that a high n-6/n-3 PUFA ratio is found in today's Western diets and it is responsible for the promotion of the pathogenesis of many diseases, including cardiovascular disease, cancer, and inflammatory and autoimmune diseases (17).
In conclusion, our study suggests that the high n-6/n-3 PUFA ratio within 5 years after menopause could be a risk factor for different diseases and this risk decreases in time with the normalization of the ratio value.
Haslam DW, James WP.Obesity. Lancet. 2005; 366(9492):1197-209. doi: 10.1016/S0140-6736(05)67483-1
World Health Organ. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000; 894:i-xii, 1-253.
Lauria L, Spinelli A, Buoncristiano M, Nardone P. Decline of childhood overweight and obesity in Italy from 2008 to 2016: results from 5 rounds of the population-based surveillance system. BMC Public Health. 2019;19(1):618. doi: 10.1186/s12889-019-6946-3.
Akazawa N, Harada K, Okawa N, Tamura K, Moriyama H. Low body mass index negatively affects muscle mass and intramuscular fat of chronic stroke survivors. PLoS One. 2019;14(1):e0211145. doi: 10.1371/journal.pone.0211145.
Frost AP, Norman Giest T, Ruta AA, Snow TK, Millard-Stafford M. Limitations of body mass index for counseling individuals with unilateral lower extremity amputation. Prosthet Orthot Int. 2017; 41(2):186-193. doi: 10.1177/0309364616650079.
Banack HR, Wactawski-Wende J, Hovey KM, Stokes A. Is BMI a valid measure of obesity in postmenopausal women? Menopause. 2018; 25(3):307-313. doi: 10.1097/GME.0000000000000989.
Polotsky HN, Polotsky AJ. Metabolic implications of menopause. Semin Reprod Med. 2010; 28(5):426-34. doi: 10.1055/s-0030-1262902.
Agostoni C, Bruzzese MG. Fatty acids: their biochemical and functional classification. Pediatr. Med. Chir. 1992; 14, 473-479.
Amézaga J, Arranz S, Urruticoechea A, Ugartemendia G, Larraioz A, Louka M, Uriarte M, Ferreri C, Tueros I. Altered Red Blood Cell Membrane Fatty Acid Profile in Cancer Patients Nutrients 2018; 10(12):1853doi: 10.3390/nu10121853.
Ferreri C, Chatgilialoglu C. Role of fatty acid-based functional lipidomics in the development of molecular diagnostic tools. Expert Rev Mol Diagn. 2012;12(7):767-80. doi: 10.1586/erm.12.73
Flury B, A First Course in Multivariate Statistics Springer-Verlag, New York, 1997.
Ferreri C, Chatgilialoglu C. Membrane Lipidomics for personalized health. in: Biochemistry. Eds: John Wiley & Sons Inc 2015; pp.1-208.
Martinelli N, Consoli L, Olivieri O. A‘desaturase hypothesis> for atherosclerosis: Janus-faced enzymes in omega-6 and omega-3 polyunsaturated fatty acid metabolism. J Nutrigenet Nutrigenomics. 2009; 2(3):129-39. doi: 10.1159/000238177.
Knez M, Stangoulis JCR, Glibetic M, Tako E. The Linoleic Acid: Dihomo-γ-Linolenic Acid Ratio (LA:DGLA)-An Emerging Biomarker of Zn Status. Nutrients. 2017; 9(8):825. doi: 10.3390/nu9080825.
Angela Liou Y, Innis SM. Dietary linoleic acid has no effect on arachidonic acid, but increases n-6 eicosadienoic acid, and lowers dihomo-gamma-linolenic and eicosapentaenoic acid in plasma of adult men. Prostaglandins Leukot Essent Fatty Acids. 2009; 80(4):201-6. doi: 10.1016/j.plefa.2009.02.003
Innes JK, Calder PC.Omega-6 fatty acids and inflammation. Prostaglandins Leukot Essent Fatty Acids. 2018; 132:41-48. doi: 10.1016/j.plefa.2018.03.004.
Araya J, Rodrigo R, Pettinelli P, Araya AV, Poniachik J, Videla LA. Decreased liver fatty acid delta-6 and delta-5 desaturase activity in obese patients. Obesity (Silver Spring). 2010; 18(7):1460-3. doi: 10.1038/oby.2009.379.
Pang SC, Wang HP, Li KY, Zhu ZY, Kang JX, Sun JH. Double Transgenesis of Humanized fat1 and fat2 Genes Promotes omega-3 Polyunsaturated Fatty Acids Synthesis in a Zebrafish Model. 2014;16(5):580-93 Mar Biotechnol (NY) doi: 10.1007/s10126-014-9577-9.Epub 2014 May 16.
Funk CD. Prostaglandins and leukotrienes: advances in eicosanoid biology. Science. 2001; 294(5548):1871-5. doi: 10.1126/science.294.5548.1871.
Simopoulos AP. The importance of the ratio of omega-6/omega-3 essential fatty acids. Biomed Pharmacother. 2002; 56(8):365-79. doi: 10.1016/s0753-3322(02)00253-6.