==== Front BMC Geriatr BMC Geriatr BMC Geriatrics 1471-2318 BioMed Central London 4040 10.1186/s12877-023-04040-8 Research Clinical utility of lipid ratios as potential predictors of metabolic syndrome among the elderly population: Birjand Longitudinal Aging Study (BLAS) Saeedi Farhad 12 Baqeri Elnaz 1 Bidokhti Ali 2 Moodi Mitra 3 Sharifi Farshad 4 Riahi Seyed Mohammad riahim61@gmail.com 5 1 grid.411701.2 0000 0004 0417 4622 Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran 2 grid.411701.2 0000 0004 0417 4622 Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran 3 grid.411701.2 0000 0004 0417 4622 Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran 4 grid.411705.6 0000 0001 0166 0922 Elderly Health Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran 5 grid.411701.2 0000 0004 0417 4622 Cardiovascular Diseases Research Center, Department of Epidemiology and Biostatistics, School of Medicine, Birjand University of Medical Sciences, Birjand, Iran 3 7 2023 3 7 2023 2023 23 4038 6 2022 15 5 2023 © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ 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. Background Elderly adults are at higher risk of developing metabolic syndrome (MetS). The present study aims to investigate the relationship between lipid ratios and MetS in the elderly population. Methods This study was conducted on elderly population of Birjand during 2018–2019. The data of this study was driven from Birjand Longitudinal Aging Study (BLAS). The participants were selected based on multistage stratified cluster sampling. Patients were categorized into quartiles according to the lipid ratios (TG/HDL-C, LDL-C/HDL-C, non-HDL/HDL-C), and the relationship between lipid ratio quartiles and MetS was determined by Logistic Regression using Odds Ratio. Finally, the optimal cut-off for each lipid ratio in MetS diagnosis was calculated according to the Area Under the Curve (AUC). Results This study included 1356 individuals, of whom 655 were men and 701 were women. In our study, the crude prevalence of MetS was 792 (58%), including 543 (77.5%) women and 249 (38%) men. Increasing trends were observed in quartiles of all lipid ratios for TC, LDL-C, TG, and DBP. TG/HDL was also the best lipid ratio to diagnose the MetS, based on NCEP ATP III criteria. One unit increased in level of TG/HDL resulted in 3.94 (OR: 3.94; 95%CI: 2.48–6.6) and 11.56 (OR: 11.56; 95%CI: 6.93–19.29) increasing risk of having MetS in quartile 3 and 4 compared to quartile 1, respectively. In men and women, the cutoff for TG/HDL was 3.5 and 3.0, respectively. Conclusions Our results showed that the TG/HDL-C is superior to the LDL-C/HDL-C and the non-HDL /HDL-C to predict MetS among the elderly adults. Keywords Metabolic syndrome Lipids Aged Frail Elderly Geriatrics issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2023 ==== Body pmcIntroduction Metabolic Syndrome (MetS) is characterized by a combination of disorders including hyperglycemia, dyslipidemia, hypertension, and abdominal obesity and shows an increasing growth pattern during the recent decades [1, 2]. MetS prevalence is highly variable based on the MetS definition Criteria and the studied population ranging between < 10–84%; though, MetS prevalence is globally estimated to be nearly 20–25% in the adults [3]. The growing prevalence of MetS was confirmed by a population-based cohort study in Iran, rising from 35.6% to 2001 to 42.5% in 2013 [4]. Studies have shown the association between MetS and obesity, higher level of income, sedentary lifestyle, and urbanization [5–8]. Also, the studies have confirmed the higher risk of cognitive impairment, diabetes mellitus type 2, and chronic renal diseases in patients with MetS [9–13]. On the other hand, accumulating evidence demonstrates the increased rate of cardiovascular diseases, all-cause mortality, and diabetes mellitus cardiovascular mortality [14–16]. MetS early diagnosis and treatment effectively prevents the development of undesired complications; thus, clinically, it is beneficial to identify the proper criteria for accurate differentiation of patients with MetS and healthy individuals [17]. The NCEP ATP III, a commonly used MetS diagnosis criteria, requires waist circumference (WC) measurement [18], which is not usually performed in clinical settings, thereby postponing the MetS early diagnosis [19]. Recently, alternative lipid ratio indices such as low-density lipoprotein cholesterol (LDL-C)/ HDL-C, triglyceride (TG)/HDL-C, and non high-density lipoprotein cholesterol /high-density lipoprotein cholesterol (non-HDL /HDL-C) are suggested for Mets diagnosis [1, 19–22]. It seems that lipid ratios (using two lipid serum levels) are clinically more practical compared with individual lipid levels in MetS diagnosis [19, 23–25], though limited studies have been conducted on the accuracy of this approach in MetS diagnosis on specific populations [19, 26–28]. The rate of MetS varies in different age groups due to physiologic factors, e.g., postmenopausal women are at higher risk of MetS due to hormonal alterations [29]; also, the prevalence of MetS among the elderly is increasing worldwide due to the age-related physiologic changes and the elderly are at higher risk of abdominal obesity and MetS [30]. Sex difference in the prevalence of MetS is clinically important and may result from sex hormone effects, leading to insulin resistance, obesity, and hypertension [31–33]. Particularly in a recent study, it’s been shown that higher level of testosterone and sex hormone binding globulin were associated with increased insulin sensitivity and decreased risk of MetS development in elderly male [34]. MetS may lead to several diseases and complications that highly affect the individuals’ health status. On the other hand, no studies have been conducted on the diagnostic accuracy of lipid ratios, especially in the elderly. The present study aims to investigate the relationship between lipid ratios and MetS in the male and female elderly population in Birjand. Methods and materials Study population and data collection This analytic epidemiologic study was conducted on the elderly population of Birjand, East of Iran during 2018–2019. The study participants were selected using random cluster sampling based on multistage stratified cluster sampling. The details about study design of Birjand Longitudinal Aging Study (BLAS) were discussed elsewhere [35]. Briefly, the dataset of this study was obtained from Sib electronic, which is a national health records dataset, and hand registry systems. This study includes urban participants from Birjand county. Inclusion and exclusion criteria. People who were 60 years old or older and were able to participate study, meaning that visiting the research center and complete the questionare and physical exam and laboratory data, were include. The people who were unable to participte study including those who were bedridden or had sever cognition disorder were excluded. Moreover, those elderly adults who were lost to follow up were excluded. The people who were not interested in participating study were excluded. Covariate After proposal approval and obtaining the research ethics code, data was obtained from the Social Determinants of Health (SDH) research center regarding the comprehensive plan for the elderly. Clinical and laboratory measurmenents were discussed elsewhere [35]. The primary outcome variable was MetS, defined according to NCP/ATP III definition [18]. We defined MetS in the presence of at least three of the following criteria based on NCP/ATP III definition: (1) waist circumference ≥ 102 cm in men and ≥ 88 cm in women, (2) triglyceride ≥ 150 mg/dL or on drug treatment for dyslipidemia, (3) HDL-C < 40 mg/dL in men and < 50 mg/dL in women, (4) blood pressure ≥ 130 mm Hg systolic blood pressure or ≥ 85 mm Hg diastolic blood pressure or on antihypertensive treatment, (5) fasting blood glucose ≥ 100 mg/dL or on antidiabetic treatment [36] . Independent variables in this study included different lipid ratios (TG/HDL, LDL/HDL, and non-HDL/HDL). The confounding variables were BMI, smoking, hypertension, and diabetes mellitus. Patients were categorized into quartiles according to the lipid ratios, and the relationship between lipid ratio quartiles and MetS was determined by Logistic Regression using Odds Ratio. Lipid ratio calculation Lipid ratios were calculated by dividing the TG, LDL-C, and TC values by HDL-C values. The unites for all the lipid variables were mg/dl. Non-HDL is calculated as TC minus HDL, so that the ratio of non-HDL/HDL equals to (TC/HDL)-1. Statistical analysis In this study, qualitative data are reported as frequency and percentage, and quantitative variables were reported as mean and standard deviation or median (first and third quartiles). The standard indices were calculated based on the WHO standard world population 2000 [37]. Chi-square or Fisher’s exact test was used to compare qualitative variables between patients with and without MetS. Data Normality was confirmed by the Kolmogorov–Smirnov test and P-P plot. T-test or MannWhitney test was used to compare the means of the quantitative variable between patients with and without MetS. Logistic regression was used to determine the relationships between lipid ratios and MetS. Lipid ratios were divided in 4 groups (quartile). The first quartile was used as a reference group against which the remaining quartiles were compared. Area Under the Curve (AUC) and Hosmer–Lemeshow test was used to goodness of fit the statistical models. ROC curve analysis was used to determine the optimal cut-off points for each lipid ratio. Youden index was calculated as follows: sensitivity (%) + specificity (%) – 100 [38]. Statistical analysis was performed using SPSS version 22, and a P-value below 0.05 was considered statistically significant. Results Characteristics are shown in Table 1. This study included 1356 individuals, of whom 655 were men and 701 were women. In our study, 792 (58%) participants were diagnosed with MetS, including 543 (77.5%) women and 249 (38%) men. The mean age of patients with MetS was 69.2 ± 7.01 years. BMI, WC, FBS, TG, SBP, and DBP were significantly higher among the MetS group. However, TC was not significantly different between the non-MetS and MetS groups. Moreover, HDL-C and LDL-C were significantly lower in the MetS group. In contrast to LDL/HDL, TG/HDL was significantly higher in the MetS group compared to the non-MetS group. non-HDL/HDL was significantly higher among the total population and women among MetS groups. Table 1 Baseline characteristics of individuals with and without MetS Variables Total (1356) Men (655) Women (701) METS - METS+ PVALUE METS- METS+ PVALUE METS- METS+ PVALUE 564(42%) 792(58%) 406 (62%) 249(38%) 158(23%) 543(77%) Age 70.4(8.1) 69.2(7.01) 0.005 71.1(8.2) 70.0(7.0) 0.046 68.6(70.6) 69.0(7.0) 0.643 BMI 24.0(4.7) 28.2(5.0) < 0.001 23.7(4.7 27.1(3.7) < 0.001 24.7(4.7) 28.7(5.4) < 0.001 WC 88.3(10.6) 100.2(10.0) < 0.001 88.3(10.6) 99.8(9.6) < 0.001 88.4(10.4) 100.5(10.3) < 0.001 FBS 97.0(26.3) 119.8(40.3) < 0.001 98.4(29.2) 125.5(37.9) < 0.001 93.5(16.0) 117.2(41.3) < 0.001 TG 129.4(46.5) 171.0(71.9) < 0.001 129.8(49.2) 179.4(78.5) < 0.001 128.4(39.0) 167.0(68.3) < 0.001 SBP 127.4(20.7) 134.9(20.2) < 0.001 131.2(20.6) 142.3(18.5) < 0.001 117.6(17.5) 131.5(20.1) < 0.001 DBP 76.3(11.2) 79.6(11.3) < 0.001 77.2(11.4 82.0(11.5) < 0.001 74.1(10.4) 78.6(11.1) < 0.001 TC 196.8(38.4) 197.9(43.2) 0.605 192.89(38.8) 189.6(47.8) 0.369 207.0(35.3) 201.8(40.3) 0.137 HDL-C 44.8(5.0) 43.0(5.1) < 0.001 44.1(4.4) 41.8(5.3) < 0.001 46.5(5.9) 43.5(4.9) < 0.001 LDL-C 125.7(34.4) 121 (37.3) 0.017 122.5(34.5) 113.4(39.7) 0.003 133.9(33.0) 124.4(35.7) 0.003 Non-HDL/HDL 4.4(0.9) 4.7 (1.1) < 0.001 4.4(1.0) 4.5(1.3) 0.058 4.5(0.9) 4.7(1.1) 0.038 TG/HDL 3.0(1.2) 4.1(2.0) < 0.001 3.0(1.3) 4.4(2.3) < 0.001 2.8(1.0) 4.0(1.8) < 0.001 LDL/HDL 2.8(0.8) 2.8(0.9) 0.817 2.8(0.8) 2.7(1.0) 0.422 2.9(0.8) 2.9(5.9) 0.761 Biomarkers and variables of MetS in different quartiles of lipid ratios are shown in Table 2. While HDL-C showed a decreasing trend, TC, LDL-C, TG, and DBP significantly raised across the quartiles of all lipid ratios (P-value < 0.001). Moreover, our results showed an increasing trend in WC and BMI across the quartiles of non-HDL/HDL and TG/HDL (P-value < 0.001). However, FBS and WHR significantly increased across the TG/HDL and LDL/HDL quartiles (P-value < 0.001). Table 2 Biomarkers and components of MetS in quartiles of lipid ratios Quartiles of non-HDLC/HDL-C Ratio Q1 (< 2.81) Q2 (2.81–3.47) Q3 (3.47–4.18) Q4 (> 4.18) P-trend N = 1356 339 (25%) 336(24.81%) 341 (25.2%) 339 (25%) Mean SD Mean SD Mean SD Mean SD WC (cm) 94.3 11.9 95.0 11.5 96.8 12.0 95.1 11.7 < 0.001 BMI 26.0 5.7 26.1 5.0 27.1 5.8 26.4 4.7 < 0.001 TC (mg/dl) 149.4 22.0 187.2 19.9 212.5 23.2 240.6 29.6 < 0.001 HDL-c (mg/dl) 45.0 4.5 45.0 4.5 44.2 4.6 40.7 5.6 < 0.001 LDL-c (mg/dl) 81.5 17.5 114.0 18.9 135.8 22.6 160.1 26.9 < 0.001 TG (mg/dl) 122.1 41.4 141.9 48.1 159.0 59.7 191.4 84.6 < 0.001 SBP (mm Hg) 76.7 11.6 77.2 10.9 78.8 11.4 80.1 11.3 < 0.001 DBP (mm Hg) 132.4 20.6 130.3 20.6 132.3 21.7 132.0 20.1 0.506 Met-total day 2.9 4.5 3.2 4.5 3.2 5.3 3.6 5.6 0.361 WHR (cm) 1.0 0.1 1.0 0.1 1.0 0.1 0.9 0.1 0.179 Hip (cm) 98.4 10.7 99.1 10.3 101 11.1 100.5 11.3 < 0.001 FBS (mg/dl) 111.3 36.5 110.7 37.1 109.6 35.1 109.6 39.1 0.902 Quartiles of TG/HDL-C Ratio Q1 (< 2.47) Q2 (2.47–3.21) Q3(3.21–4.25) Q4 (> 4.25) P-trend N = 1356 338 (24.9%) 343(25.3%) 340 (25.1%) 335 (24.7%) Mean SD Mean SD Mean SD Mean SD WC (cm) 91.5 12.8 94.2 18.6 97.1 11.2 98.5 10.3 < 0.001 BMI 24.7 5.0 26.4 5.3 27.2 5.6 27.4 4.9 < 0.001 TC (mg/dl) 178.5 37.5 198.1 38.6 197.8 40.2 215.6 40.2 < 0.001 HDL-c (mg/dl) 45.6 4.8 45.1 4.2 43.0 4.6 41.2 5.6 < 0.001 LDL-c (mg/dl) 114.1 33.6 125.5 36.3 123.7 37.4 128.4 36.0 < 0.001 TG (mg/dl) 89.9 14.3 129.4 13.8 158.3 21.0 238.1 69.4 < 0.001 SBP (mm Hg) 76.0 11.0 78.8 11.9 78.1 10.8 80.0 11.6 < 0.001 DBP (mm Hg) 130.4 21.8 132.4 21.3 130.7 19.7 133.6 20.0 0.142 Met-total day 3.0 4.3 3.7 6.2 3.0 4.0 3.1 5.2 0.273 WHR (cm) 0.9 0.1 0.9 0.1 1.0 0.1 1.0 0.1 < 0.001 Hip (cm) 96.4 10.7 99.5 10.6 101.2 11.0 101.9 10.5 < 0.001 FBS (mg/dl) 101.5 24.7 108.2 38.4 110.7 31.5 120.9 47.0 < 0.001 Quartiles of LDL/HDL ratio Q1(< 2.15) Q2 (2.15–2.80) Q3 (2.80–3.42) Q4 (> 3.42) P-trend N = 1356 338 (24.9%) 340 (25.1%) 338 (24.9%) 340 (25.1%) Mean SD Mean SD Mean SD Mean SD WC (cm) 95.4 11.7 94.9 11.5 96.5 11.7 94.5 12.3 0.150 BMI 26.4 5.7 26.3 5.3 26.7 5.3 26.4 5.0 0.823 TC (mg/dl) 151.7 24.4 187.1 23 210.6 25.7 240.4 28.3 < 0.001 HDL-c (mg/dl) 44.7 4.5 44.8 4.9 44.1 4.7 41.3 5.5 < 0.001 LDL-c (mg/dl) 78.5 14.4 111.7 15.2 136.3 16.4 165 22.1 < 0.001 TG (mg/dl) 140.0 62.9 149.1 59.6 151.9 59.7 173.5 75.4 < 0.001 SBP (mm Hg) 133.2 20.1 130.3 21.9 132.4 21.2 131.2 19.7 0.259 DBP (mm Hg) 77.2 11.7 77.0 11.5 78.9 11.5 79.8 10.6 < 0.001 Met-total day 2.9 4.7 3.0 3.9 3.5 5.6 3.5 5.6 0.207 WHR (cm) 1.0 0.1 1.0 0.1 1.0 0.1 0.9 0.1 0.030 Hip (cm) 99.2 10.6 99.2 10.7 100.7 10.6 100.0 11.6 0.222 FBS (mg/dl) 116.9 42.3 108.2 33.6 107.9 31.8 108.1 38.4 0.002 The probability of developing MetS was not statistically significant across the quartiles of all lipid ratios (Table 3). However, TG/HDL was the best predictable lipid ratio for the incidence of MetS. It was shown that a 1 unit increased in level of TG/HDL resulted in 3.94 (OR: 3.94; 95%CI: 2.48–6.6) and 11.56 (OR: 11.56; 95%CI: 6.93–19.29) increasing risk of having MetS in quartile 3 and 4 compared to quartile 1, respectively. This increasing trend was also observed in both men and women groups. Table 3 The relation between chance of MetS development and lipid ratios OR (95%CI) OR (95%CI) OR (95%CI) AUC HLT Quartiles of non-HDL/HDL-C Ratio Q1 (< 2.81) Q2 (2.81–3.47) Q3 (3.47–4.18) Q4 (> 4.18) Total Reference 1.11 (0.72–1.71) 1.56 (1.01–2.41) 2.98 (1.90–4.69) 0.90 (0.88–0.91) 0.854 Men Reference 1.21 (0.66–2.19) 1.82 (1.00-3.31) 3.14 (1.79–5.28) 0.88 (0.85–0.90) 0.475 women Reference 0.93 (0.48–1.80) 1.25 (0.65–2.40) 2.5 (1.26–4.94) 0.88 (0.85–0.90) 0.926 Quartiles of TG/HDL-C Ratio Q1 (< 2.47) Q2 (2.47–3.21) Q3(3.21–4.25) Q4 (> 4.25) Total Reference 1.11 (0.67–1.60) 3.94 (2.48–6.6) 11.56 (6.93–19.29) 0.92 (0.91–0.93) 0.388 Men Reference 1.21 (0.45–1.78) 4.45 (2.33–8.50) 12.19 (6.16–24.12) 0.90 (0.88–0.93) 0.198 women Reference 0.93 (0.62–2.08) 3.43 (1.74–6.76) 9.36 (4.27–20.48) 0.90 (0.88–0.92) 0.556 Quartiles of LDL/HDL-C Ratio Q1 (< 2.15) Q2 (2.15–2.80) Q3 (2.80–3.42) Q4 (> 3.42) Total Reference 1.03 (0.67–1.60) 1.3 (0.84-2.00) 1.77 (1.12–2.77) 0.89 (0.88–0.91) 0.884 Men Reference 1.01 (0.56–1.81) 1.12 (0.63-2.00) 1.67 (0.91–3.03) 0.87 (0.84–0.90) 0.408 women Reference 1.12 (0.56–2.22) 1.55 (0.78–3.08) 1.84 (0.91–3.70) 0.87 (0.84–0.90) 0.855 The AUC of TG/HDL was greater than other lipid ratios, suggesting that TG/HDL is a more reliable MetS predictor (Fig. 1). Including the confounding variables, the AUC of LDL/HDL, TG/HDL, and non-HDL/HDL for diagnosis of MetS were 0.89 (0.88–0.91), 0.92 (0.91 to 0.93), and 0.90 (0.88 to 0.91), respectively (Fig. 1A). Similar patterns were found for men and women, with an approximately same AUC for both groups (Fig. 1B C). The AUC of LDL/HDL, TG/HDL, and non-HDL/HDL for diagnosis of MetS in men were 0.87 (0.84–0.90), 0.90 (0.88 to 0.93), and 0.88 (0.85 to 0.90), respectively. The AUC of LDL/HDL, TG/HDL, and non-HDL/HDL for diagnosis of MetS in women were 0.87 (0.84–0.90), 0.90 (0.88 to 0.92), and 0.88 (0.85 to 0.90), respectively. Moreover, the ROC models of asscociation between lipid ratios and MetS in women and men, after excluding confounding variables, were shown in Figs. 2 and 3. In women, the AUC for non-HDL/HDL, TG/HDL, LDL/HDL were 0.55 (95%CI: 0.50–0.60), 0.73 (95%CI: 0.68–0.77), and 0.49 (95%CI: 0.44–0.54), respectively. The P-value of ROC model for non-HDL/HDL, TG/HDL, LDL/HDL after excluding confounding variables were 0.05, 0.001, and 0.69 in women, respectively. In men, the AUC for non-HDL/HDL, TG/HDL, LDL/HDL were 0.53 (95%CI: 0.48–0.57), 0.74 (95%CI: 0.70–0.78), and 0.47 (95%CI: 0.42–0.51), respectively. The P-value of ROC model for non-HDL/HDL, TG/HDL, LDL/HDL HDL after excluding confounding variables were 0.26, 0.001, and 0.13 in men, respectively. In men and women, the cutoff for TG/HDL was 3.5 and 3.0, respectively. Youden index of TG/HDL for men and women was 40 and 39, respectively. The ROC curve of TC/HDL would overlap non-HDL/HDL. The quartiles of non-HDL/HDL would be as the same as TC/HDL. So there would be no differences in ROC model and quartiles of non-HDL/HDL compared to TC/HDL. Fig. 1 ROC curve for the value of lipid ratios for prediction of MetS among general population (A), men (B), and women (C) Fig. 2 ROC models of asscociation between lipid ratios and MetS in women after excluding confounding variables Fig. 3 ROC models of asscociation between lipid ratios and MetS in men after excluding confounding variables Discussion This study aimed to investigate the association of MetS with lipid ratios among the elderly. It was found that BMI, WC, SBP, DBP, WHR were significantly higher in the MetS group compared to the non-MetS group. Moreover, increasing trends were observed in quartiles of all lipid ratios for TC, LDL-C, TG, and DBP. TG/HDL was also the best lipid ratio to predict the diagnosis of MetS, based on NCEP ATP III criteria. Sex differences in metabolic syndrome prevalence might be due to abdominal obesity, hormone modulation, and glucose metabolism [39]. Moreover, the inflammatory process responsible for MetS might be sex-specific. A recent study showed increased level of inteleukin-6, cytokine, and leptin in men as well as decreased level of adiponectin in women [40]. Dyslipidemia has an important role in the pathophysiology of MetS. Abdominal obesity might give rise to hypertrophic adipocytes, inducing insulin resistance. Furthermore, decreased retention of free fatty acid in adipocytes, increased production of very low density lipoprotein (VLDL) apo B-100, decreased catabolism of apo B comprising particles, and increased catabolism of HDL-apo A-I may all result in increased levels of free fatty acid, VLDL, and decreased levels of HDL-C in plasma [41]. Cholesterol is transported by various lipoproteins in plasma. The main carrier of cholesterol from liver is LDL, by which cholesterol deposits in intima layers of arteris, resulting in formation of atherogenic plaques [42]. However, HDL transports cholesterol to liver and decreases the risk of atherosclerorsis [42]. The plasma level of cholesterol increases with aging, leading to increased risk of cardiovascular disease [43, 44]. MetS might give rise to microvascular damage, leading to endothelial dysfunction, increased vascular resistance, atherosclerosis,and hypertension [45]. In addition, the level of adiponectin decreases in patients with MetS,which might eventually result in coronary heart disease [46, 47]. A growing body of studies showed that MetS is prevalent among the elderly in Iran [48–50]. Although there is some conflicting evidence regarding MetS mortality in the elderly [51], it is recommended that they undergo MetS screening to lower the probability of developing severe complications as well as the cost to the public health system [52]. It has been shown that TG/HDL can be used as an accurate and reliable tool to predict MetS [17, 21, 53]. Interestingly, the application of this ratio to predict CVD was proved in some studies [54, 55]. Insulin-resistant patients with TG/HDL ≥ 3.0 are diagnosed with MetS according to NCEP and IDF criteria [56]. In line with our findings, the AUC for TG/HDL was higher than other lipid ratios in predicting MetS [57, 58]. Thus, TG/HDL can be utilized as a diagnostic tool in addition to the other well-established diagnostic criteria for MetS. Although WC is one of the main diagnostic criteria of MetS, it is not routinely measured during primary care visits [59]. It is well-established that WC, representing visceral lipid, is a health risk factor [60]. Recently, it was reported that WC is a better predictor of MetS than percentage body fat measured by impedance method among elderly [61]. We showed that in both men and women, the cutoff for non-HDL/HDL, TG/HDL, and LDL/HDL were 0.88 (0.85–0.90), 0.90 (0.88–0.93), and 0.87 (0.84–0.90), respectively. Hadaegh et al. revealed the optimal cutoff values for TG/HDL-C (4.7 in men and 3.7 in women) and TC/HDL (5.3 in both men and women) in order to predict the incidence of diabetes in men and women [62]. In another study, they also found that MetS in males with a TG/HDL-C level of 2.8–4.4 is five times greater than in those with a TG/HDL-C level of less than 2.8 [63]. Diagnostic criteria of MetS components might be different in various population. There is limited evidence of lipid ratios (i.e. TG/HDL, LDL/HDL, and non-HDL/HDL) for diagnosing MetS in the Iranian people. Given that early diagnosis of MetS might prevent severe complications, it is imperative to develop some practical criteria to distinguish healthy adults from those with MetS. Since the laboratory data for lipid profiles including TG and HDL are monitored routinely by health professionals in Iran, TG/HDL can play a pivotal role in screening MetS. However, it is worth mentioning that it should not be replaced with well-known criteria for MetS diagnosis. Strenghts and limitations The first strength of our study is that it includes a large sample of elderly adults. Second, this study shows a primary care approach in geriatric medicine. The most important limitation of our study was that the bedridden elderly were not included in this study. Second, this is a cross sectional study, hence we recommend further prospective study to evaluate the association of lipid ratio with metablic syndrome. Third, since there was no information about sedentary life style of elderly people in our study, it was not included as cofounding variables. Fourth, because rural people were not included in this study, there was no information regarding the distribution of people based on rural and urban area. Conclusion In conclusion, the results of this study showed a significant association between lipid ratios and metabolic syndrome among the elderly in Iran. Our results illustrated that the TG/HDL-C ratio is superior to the LDL-C/HDL-C ratio and the non-HDL/HDL ratio to predict metabolic syndrome among the elderly based on NCEP ATP III criteria. Decleration. Acknowledgements The authors thanks Birjand University of Medical Sciences and Endocrinology and Metabolism Research Institute of Tehran University of Medical Sciences for support of this study. The authors also want to thanks Dr. Rezaei for her guidance in this study. Authors’ contributions FS, EB, SMR and AB contributed in writing the draft. MM, FSH, and SMR critically reviewed the manuscript. EB, FS, SMR equally contributed to this study. Funding We declare that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profits sectors. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to the privacy of participants but are available from the corresponding author on reasonable request. Declarations Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate This study was approved by Ethics Committee of Birjand University of Medical Sciences. Informed consent was obtained from all subjects and/or their legal guardian(s). All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Amirkalali B, Fakhrzadeh H, Sharifi F, Kelishadi R, Zamani F, Asayesh H et al. Prevalence of metabolic syndrome and its components in the iranian adult population: a systematic review and meta-analysis. Iran red crescent Med J. 2015;17(12). 2. Mokhayeri Y Riahi SM Rahimzadeh S Pourhoseingholi MA Hashemi-Nazari SS Metabolic syndrome prevalence in the iranian adult’s general population and its trend: a systematic review and meta-analysis of observational studies Diabetes & metabolic syndrome 2018 12 3 441 53 10.1016/j.dsx.2017.12.023 29292185 3. Kaur J. A comprehensive review on metabolic syndrome. Cardiology research and practice. 2014;2014. 4. Khosravi-Boroujeni H Sarrafzadegan N Sadeghi M Roohafza H Talaei M Ng S-K Secular trend of metabolic syndrome and its components in a cohort of iranian adults from 2001 to 2013 Metab Syndr Relat Disord 2017 15 3 137 44 10.1089/met.2016.0073 28135122 5. Edwardson CL Gorely T Davies MJ Gray LJ Khunti K Wilmot EG Association of sedentary behaviour with metabolic syndrome: a meta-analysis PLoS ONE 2012 7 4 e34916 10.1371/journal.pone.0034916 22514690 6. O’Neill S O’Driscoll L Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies Obes Rev 2015 16 1 1 12 10.1111/obr.12229 25407540 7. Ranasinghe P Mathangasinghe Y Jayawardena R Hills A Misra A Prevalence and trends of metabolic syndrome among adults in the asia-pacific region: a systematic review BMC Public Health 2017 17 1 1 9 10.1186/s12889-017-4041-1 28049454 8. Riahi SM Moamer S Namdari M Mokhayeri Y Pourhoseingholi MA Hashemi-Nazari SS Patterns of clustering of the metabolic syndrome components and its association with coronary heart disease in the multi-ethnic study of atherosclerosis (MESA): a latent class analysis Int J Cardiol 2018 271 13 8 10.1016/j.ijcard.2018.05.080 29885819 9. Chen J Muntner P Hamm LL Jones DW Batuman V Fonseca V The metabolic syndrome and chronic kidney disease in US adults Ann Intern Med 2004 140 3 167 74 10.7326/0003-4819-140-3-200402030-00007 14757614 10. Solymoss BC Bourassa MG Campeau L Sniderman A Marcil M Lespérance J Effect of increasing metabolic syndrome score on atherosclerotic risk profile and coronary artery disease angiographic severity Am J Cardiol 2004 93 2 159 64 10.1016/j.amjcard.2003.09.032 14715340 11. Varounis C Rallidis LS Franco OH Lekakis J Prevalence of metabolic syndrome and association with burden of atherosclerotic disease in patients with stable coronary artery disease Curr Med Res Opin 2016 32 6 1175 81 10.1185/03007995.2016.1163257 26950061 12. Wilson PW D’Agostino RB Parise H Sullivan L Meigs JB Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus Circulation 2005 112 20 3066 72 10.1161/CIRCULATIONAHA.105.539528 16275870 13. Yaffe K Weston AL Blackwell T Krueger KA The metabolic syndrome and development of cognitive impairment among older women Arch Neurol 2009 66 3 324 8 10.1001/archneurol.2008.566 19273750 14. Galassi A Reynolds K He J Metabolic syndrome and risk of cardiovascular disease: a meta-analysis Am J Med 2006 119 10 812 9 10.1016/j.amjmed.2006.02.031 17000207 15. Gami AS Witt BJ Howard DE Erwin PJ Gami LA Somers VK Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies J Am Coll Cardiol 2007 49 4 403 14 10.1016/j.jacc.2006.09.032 17258085 16. Kassi E Pervanidou P Kaltsas G Chrousos G Metabolic syndrome: definitions and controversies BMC Med 2011 9 1 1 13 10.1186/1741-7015-9-48 21219637 17. Rezapour M Shahesmaeili A Hossinzadeh A Zahedi R Najafipour H Gozashti MH Comparison of lipid ratios to identify metabolic syndrome Arch Iran Med 2018 21 12 572 7 30634854 18. Detection, NCEPEPo. Adults ToHBCi. Third report of the national cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III): The Program; 2002. 19. Gasevic D Frohlich J Mancini GJ Lear SA Clinical usefulness of lipid ratios to identify men and women with metabolic syndrome: a cross-sectional study Lipids Health Dis 2014 13 1 1 10 10.1186/1476-511X-13-159 20. Chen B-D Yang Y-N Ma Y-T Pan S He C-H Liu F Waist-to-height ratio and triglycerides/high-density lipoprotein cholesterol were the optimal predictors of metabolic syndrome in Uighur men and women in Xinjiang, China Metab Syndr Relat Disord 2015 13 5 214 20 10.1089/met.2014.0146 25781351 21. Cordero A Laclaustra M León M Casasnovas JA Grima A Luengo E Comparison of serum lipid values in subjects with and without the metabolic syndrome Am J Cardiol 2008 102 4 424 8 10.1016/j.amjcard.2008.03.079 18678299 22. Taverna MJ Martinez-Larrad MT Frechtel GD Serrano-Rios M Lipid accumulation product: a powerful marker of metabolic syndrome in healthy population Eur J Endocrinol 2011 164 4 559 10.1530/EJE-10-1039 21262912 23. Eliasson B Cederholm J Eeg-Olofsson K Svensson A-M Zethelius B Gudbjörnsdottir S Clinical usefulness of different lipid measures for prediction of coronary heart disease in type 2 diabetes: a report from the Swedish National Diabetes Register Diabetes Care 2011 34 9 2095 100 10.2337/dc11-0209 21775750 24. Ingelsson E Schaefer EJ Contois JH McNamara JR Sullivan L Keyes MJ Clinical utility of different lipid measures for prediction of coronary heart disease in men and women JAMA 2007 298 7 776 85 10.1001/jama.298.7.776 17699011 25. Wang T-D Chen W-J Chien K-L Su SS-Y Hsu H-C Chen M-F Efficacy of cholesterol levels and ratios in predicting future coronary heart disease in a chinese population Am J Cardiol 2001 88 7 737 43 10.1016/S0002-9149(01)01843-4 11589839 26. Essiarab F Taki H Lebrazi H Sabri M Saile R Usefulness of lipid ratios and atherogenic index of plasma in obese moroccan women with or without metabolic syndrome Ethn Dis 2014 24 2 207 12 24804368 27. Kim SW Jee JH Kim HJ Jin S-M Suh S Bae JC Non-HDL-cholesterol/HDL-cholesterol is a better predictor of metabolic syndrome and insulin resistance than apolipoprotein B/apolipoprotein A1 Int J Cardiol 2013 168 3 2678 83 10.1016/j.ijcard.2013.03.027 23545148 28. Kimm H, Lee SW, Lee HS, Shim KW, Cho CY, Yun JE et al. Associations between lipid measures and metabolic syndrome, insulin resistance and adiponectin: usefulness of lipid ratios in korean men and women. Circ J. 2010:1003050635-. 29. Saklayen MG The global epidemic of the metabolic syndrome Curr Hypertens Rep 2018 20 2 1 8 10.1007/s11906-018-0812-z 29349522 30. Kazemi T Sharifzadeh G Zarban A Fesharakinia A Comparison of components of metabolic syndrome in premature myocardial infarction in an iranian population: a case-control study Int J Prev Med 2013 4 1 110 23411742 31. Yeung EH Zhang C Mumford SL Ye A Trevisan M Chen L Longitudinal study of insulin resistance and sex hormones over the menstrual cycle: the BioCycle Study J Clin Endocrinol Metab 2010 95 12 5435 42 10.1210/jc.2010-0702 20843950 32. Brown LM Gent L Davis K Clegg DJ Metabolic impact of sex hormones on obesity Brain Res 2010 1350 77 85 10.1016/j.brainres.2010.04.056 20441773 33. Connelly PJ, Casey H, Montezano AC, Touyz RM, Delles C. Sex steroids receptors, hypertension, and vascular ageing. J Hum Hypertens. 2021. 34. Muller M Grobbee DE den Tonkelaar I Lamberts SW van der Schouw YT Endogenous sex hormones and metabolic syndrome in aging men J Clin Endocrinol Metab 2005 90 5 2618 23 10.1210/jc.2004-1158 15687322 35. Moodi M, Firoozabadi MD, Kazemi T, Payab M, Ghaemi K, Miri MR et al. Birjand longitudinal aging study (BLAS): the objectives, study protocol and design (wave I: baseline data gathering). 2020;19(1):551–9. 36. Huang PL A comprehensive definition for metabolic syndrome Dis Models Mech 2009 2 5–6 231 7 10.1242/dmm.001180 37. Ahmad OBB-PC, Lopez AD, Murray CJ, Lozano R, Inoue M. Age standardization of rates: a new WHO standard. Geneva: World Health Organization. 2001;31:1–14. 38. Ruopp MD Perkins NJ Whitcomb BW Schisterman EF Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection Biometrical J Biometrische Z 2008 50 3 419 30 10.1002/bimj.200710415 39. Pradhan AD Sex differences in the metabolic syndrome: implications for cardiovascular health in women Clin Chem 2014 60 1 44 52 10.1373/clinchem.2013.202549 24255079 40. Horst Rt M ICLvd, Schraa K Aguirre-Gamboa R Jaeger M Smeekens SP Arteriosclerosis Thromb Vascular Biology 2020 40 7 1787 800 10.1161/ATVBAHA.120.314508 41. Kolovou GD Anagnostopoulou KK Cokkinos DV Pathophysiology of dyslipidaemia in the metabolic syndrome Postgrad Med J 2005 81 956 358 66 10.1136/pgmj.2004.025601 15937200 42. Félix-Redondo FJ Grau M Fernández-Bergés D Cholesterol and cardiovascular disease in the elderly. Facts and gaps Aging and disease 2013 4 3 154 69 23730531 43. Bertolotti M Lancellotti G Mussi C Management of high cholesterol levels in older people Geriatr Gerontol Int 2019 19 5 375 83 10.1111/ggi.13647 30900369 44. Rubin SM Sidney S Black DM Browner WS Hulley SB Cummings SR High blood cholesterol in elderly men and the excess risk for coronary heart disease Ann Intern Med 1990 113 12 916 20 10.7326/0003-4819-113-12-916 2240916 45. CÓ‘toi AF, Pârvu AE, Andreicuț AD, Mironiuc A, CrÓ‘ciun A, CÓ‘toi C et al. Metabolically healthy versus unhealthy morbidly obese: chronic inflammation, nitro-oxidative stress, and insulin resistance. Nutrients. 2018;10(9). 46. Kajikawa Y Ikeda M Takemoto S Tomoda J Ohmaru N Kusachi S Association of circulating levels of leptin and adiponectin with metabolic syndrome and coronary heart disease in patients with various coronary risk factors Int Heart J 2011 52 1 17 22 10.1536/ihj.52.17 21321463 47. Reddy P Lent-Schochet D Ramakrishnan N McLaughlin M Jialal I Metabolic syndrome is an inflammatory disorder: a conspiracy between adipose tissue and phagocytes Clin Chim Acta 2019 496 35 44 10.1016/j.cca.2019.06.019 31229566 48. Sarrafzadegan N Gharipour M Sadeghi M Khosravi AR Tavassoli AA Metabolic syndrome in iranian elderly ARYA Atheroscler 2012 7 4 157 61 23205049 49. Salari N Doulatyari PK Daneshkhah A Vaisi-Raygani A Jalali R Jamshidi Pk The prevalence of metabolic syndrome in cardiovascular patients in Iran: a systematic review and meta-analysis Diabetol Metab Syndr 2020 12 1 96 10.1186/s13098-020-00605-4 33292427 50. Ferns GAA Ghayour-Mobarhan M Metabolic syndrome in Iran: a review Translational Metabolic Syndrome Research 2018 1 10 22 10.1016/j.tmsr.2018.04.001 51. Ju S-Y, Lee J-Y, Kim D-H. Association of metabolic syndrome and its components with all-cause and cardiovascular mortality in the elderly: a meta-analysis of prospective cohort studies. Medicine. 2017;96(45). 52. Konz HW Meesters PD Paans NP van Grootheest DS Comijs HC Stek ML Screening for metabolic syndrome in older patients with severe mental illness Am J geriatric psychiatry: official J Am Association Geriatric Psychiatry 2014 22 11 1116 20 10.1016/j.jagp.2014.01.011 53. Gasevic D Frohlich J Mancini GJ Lear SA Clinical usefulness of lipid ratios to identify men and women with metabolic syndrome: a cross-sectional study Lipids Health Dis 2014 13 159 10.1186/1476-511X-13-159 25300321 54. da Luz PL Favarato D Faria-Neto JR Jr Lemos P Chagas ACP High ratio of triglycerides to HDL-cholesterol predicts extensive coronary disease Clin (Sao Paulo) 2008 63 4 427 32 10.1590/S1807-59322008000400003 55. Salazar MR Carbajal HA Espeche WG Aizpurúa M Leiva Sisnieguez CE March CE Identifying cardiovascular disease risk and outcome: use of the plasma triglyceride/high-density lipoprotein cholesterol concentration ratio versus metabolic syndrome criteria J Intern Med 2013 273 6 595 601 10.1111/joim.12036 23331522 56. Marotta T Russo BF Ferrara LA Triglyceride-to-HDL-cholesterol ratio and metabolic syndrome as contributors to cardiovascular risk in overweight patients Obes (Silver Spring Md) 2010 18 8 1608 13 10.1038/oby.2009.446 57. Moriyama K Associations between the triglyceride to high-density lipoprotein cholesterol ratio and metabolic syndrome, insulin resistance, and Lifestyle Habits in healthy japanese Metab Syndr Relat Disord 2020 18 5 260 6 10.1089/met.2019.0123 32191558 58. Chu S-Y Jung J-H Park M-J Kim S-H Risk assessment of metabolic syndrome in adolescents using the triglyceride/high-density lipoprotein cholesterol ratio and the total cholesterol/high-density lipoprotein cholesterol ratio Ann Pediatr Endocrinol Metab 2019 24 1 41 8 10.6065/apem.2019.24.1.41 30943679 59. Koh JH Koh SB Lee MY Jung PM Kim BH Shin JY Optimal waist circumference cutoff values for metabolic syndrome diagnostic criteria in a korean rural population J Korean Med Sci 2010 25 5 734 7 10.3346/jkms.2010.25.5.734 20436710 60. Lean ME Han TS Morrison CE Waist circumference as a measure for indicating need for weight management BMJ 1995 311 6998 158 61 10.1136/bmj.311.6998.158 7613427 61. Wannamethee SG Shaper AG Morris RW Whincup PH Measures of adiposity in the identification of metabolic abnormalities in elderly men Am J Clin Nutr 2005 81 6 1313 21 10.1093/ajcn/81.6.1313 15941881 62. Hadaegh F Hatami M Tohidi M Sarbakhsh P Saadat N Azizi F Lipid ratios and appropriate cut off values for prediction of diabetes: a cohort of iranian men and women Lipids Health Dis 2010 9 85 10.1186/1476-511X-9-85 20712907 63. Hadaegh F Khalili D Ghasemi A Tohidi M Sheikholeslami F Azizi F Triglyceride/HDL-cholesterol ratio is an independent predictor for coronary heart disease in a population of iranian men. Nutrition, metabolism, and cardiovascular diseases NMCD 2009 19 6 401 8 19091534