Associations between Metabolic Syndrome and Obesity-Related Indices and Bone Mineral Density T-Score in Hemodialysis Patients
Abstract
:1. Introduction
2. Subjects and Methods
2.1. Study Patients and Design
2.2. BMD and Body Composition Measurements
2.3. Collection of Demographic, Medical, and Laboratory Data
2.4. Definition of MetS
2.5. Calculations of Obesity-Related Indices
2.6. Statistical Analysis
3. Results
3.1. Comparisons of the Baseline Characteristics of the HD Patients with and without MetS
3.2. Associations between MetS and Its Components and T-Score in All Study Patients
3.3. Associations between Obesity-Related Indices and T-Score in the Patients with MetS
- Adjusted for age, sex, diabetes, hypertension, log HD duration, fasting glucose, albumin, hemoglobin, log TG, total cholesterol, HDL-C, LDL-C, CaXP product, and log PTH for BMI, WHR, WHtR, AVI, BRI, CI, and BAI.
- Adjusted for age, sex, diabetes, hypertension, log HD duration, fasting glucose, albumin, hemoglobin, total cholesterol, LDL-C, CaXP product, and log PTH for VAI.
- Adjusted for age, sex, diabetes, hypertension, log HD duration, fasting glucose, albumin, hemoglobin, total cholesterol, HDL-C, LDL-C, CaXP product, and log PTH for LAP.
3.4. Associations between Obesity-Related Indices and T-Score in the Patients without MetS
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sugimoto, T.; Sato, M.; Dehle, F.C.; Brnabic, A.J.; Weston, A.; Burge, R. Lifestyle-Related Metabolic Disorders, Osteoporosis, and Fracture Risk in Asia: A Systematic Review. Value Health Reg. Issues 2016, 9, 49–56. [Google Scholar] [CrossRef] [Green Version]
- Compston, J.E.; McClung, M.R.; Leslie, W.D. Osteoporosis. Lancet 2019, 393, 364–376. [Google Scholar] [CrossRef]
- Pouresmaeili, F.; Kamalidehghan, B.; Kamarehei, M.; Goh, Y.M. A comprehensive overview on osteoporosis and its risk factors. Ther. Clin. Risk Manag. 2018, 14, 2029–2049. [Google Scholar] [CrossRef] [Green Version]
- Nickolas, T.L.; Leonard, M.B.; Shane, E. Chronic kidney disease and bone fracture: A growing concern. Kidney Int. 2008, 74, 721–731. [Google Scholar] [CrossRef] [Green Version]
- Slouma, M.; Sahli, H.; Bahlous, A.; Laadhar, L.; Smaoui, W.; Rekik, S.; Gharsallah, I.; Sallami, M.; Moussa, F.B.; Elleuch, M.; et al. Mineral bone disorder and osteoporosis in hemodialysis patients. Adv. Rheumatol. 2020, 60, 15. [Google Scholar] [CrossRef] [PubMed]
- Khan, M.I.; Syed, G.M.; Khan, A.I.; Sirwal, I.A.; Anwar, S.K.; Al-Oufi, A.R.; Balbaid, K.A. Mean bone mineral density and frequency of occurrence of osteopenia and osteoporosis in patients on hemodialysis: A single-center study. Saudi J. Kidney Dis. Transplant. 2014, 25, 38–43. [Google Scholar] [CrossRef]
- Moe, S.M. Renal Osteodystrophy or Kidney-Induced Osteoporosis? Curr. Osteoporos. Rep. 2017, 15, 194–197. [Google Scholar] [CrossRef]
- Evenepoel, P.; Cunningham, J.; Ferrari, S.; Haarhaus, M.; Javaid, M.K.; Lafage-Proust, M.H.; Prieto-Alhambra, D.; Torres, P.U.; Cannata-Andia, J.; European Renal Osteodystrophy workgroup aiotCKDMBDwgotERAE; et al. European Consensus Statement on the diagnosis and management of osteoporosis in chronic kidney disease stages G4-G5D. Nephrol. Dial. Transplant. 2021, 36, 42–59. [Google Scholar] [CrossRef]
- Hwang, L.C.; Bai, C.H.; Chen, C.J. Prevalence of obesity and metabolic syndrome in Taiwan. J. Formos. Med. Assoc. 2006, 105, 626–635. [Google Scholar] [CrossRef] [Green Version]
- Grundy, S.M. Metabolic syndrome update. Trends Cardiovasc. Med. 2016, 26, 364–373. [Google Scholar] [CrossRef]
- Wong, S.K.; Chin, K.Y.; Suhaimi, F.H.; Ahmad, F.; Ima-Nirwana, S. The Relationship between Metabolic Syndrome and Osteoporosis: A Review. Nutrients 2016, 8, 347. [Google Scholar] [CrossRef] [Green Version]
- Chin, K.Y.; Chan, C.Y.; Subramaniam, S.; Muhammad, N.; Fairus, A.; Ng, P.Y.; Jamil, N.A.; Aziz, N.A.; Ima-Nirwana, S.; Mohamed, N. Positive association between metabolic syndrome and bone mineral density among Malaysians. Int. J. Med. Sci. 2020, 17, 2585–2593. [Google Scholar] [CrossRef] [PubMed]
- Do Carmo, L.; Harrison, D.G. Hypertension and osteoporosis: Common pathophysiological mechanisms. Med. Nov. Technol. Devices 2020, 8, 100047. [Google Scholar] [CrossRef]
- Tanaka, K.; Yamaguchi, T.; Kanazawa, I.; Sugimoto, T. Effects of high glucose and advanced glycation end products on the expressions of sclerostin and RANKL as well as apoptosis in osteocyte-like MLO-Y4-A2 cells. Biochem. Biophys. Res. Commun. 2015, 461, 193–199. [Google Scholar] [CrossRef]
- Dawodu, D.; Patecki, M.; Dumler, I.; Haller, H.; Kiyan, Y. oxLDL inhibits differentiation of mesenchymal stem cells into osteoblasts via the CD36 mediated suppression of Wnt signaling pathway. Mol. Biol. Rep. 2019, 46, 3487–3496. [Google Scholar] [CrossRef]
- Frommer, K.W.; Hasseli, R.; Schaffler, A.; Lange, U.; Rehart, S.; Steinmeyer, J.; Rickert, M.; Sarter, K.; Zaiss, M.M.; Culmsee, C.; et al. Free Fatty Acids in Bone Pathophysiology of Rheumatic Diseases. Front. Immunol. 2019, 10, 2757. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jankowska, E.A.; Rogucka, E.; Medras, M. Are general obesity and visceral adiposity in men linked to reduced bone mineral content resulting from normal ageing? A population-based study. Andrologia 2001, 33, 384–389. [Google Scholar] [CrossRef]
- De Laet, C.; Kanis, J.A.; Oden, A.; Johanson, H.; Johnell, O.; Delmas, P.; Eisman, J.A.; Kroger, H.; Fujiwara, S.; Garnero, P.; et al. Body mass index as a predictor of fracture risk: A meta-analysis. Osteoporos. Int. 2005, 16, 1330–1338. [Google Scholar] [CrossRef]
- Adami, S.; Braga, V.; Zamboni, M.; Gatti, D.; Rossini, M.; Bakri, J.; Battaglia, E. Relationship between lipids and bone mass in 2 cohorts of healthy women and men. Calcif. Tissue Int. 2004, 74, 136–142. [Google Scholar] [CrossRef]
- Yamaguchi, T.; Sugimoto, T.; Yano, S.; Yamauchi, M.; Sowa, H.; Chen, Q.; Chihara, K. Plasma lipids and osteoporosis in postmenopausal women. Endocr. J. 2002, 49, 211–217. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hernandez, J.L.; Olmos, J.M.; Pariente, E.; Martinez, J.; Valero, C.; Garcia-Velasco, P.; Nan, D.; Llorca, J.; Gonzalez-Macias, J. Metabolic syndrome and bone metabolism: The Camargo Cohort study. Menopause 2010, 17, 955–961. [Google Scholar] [CrossRef]
- Kinjo, M.; Setoguchi, S.; Solomon, D.H. Bone mineral density in adults with the metabolic syndrome: Analysis in a population-based U.S. sample. J. Clin. Endocrinol. Metab. 2007, 92, 4161–4164. [Google Scholar] [CrossRef] [Green Version]
- von Muhlen, D.; Safii, S.; Jassal, S.K.; Svartberg, J.; Barrett-Connor, E. Associations between the metabolic syndrome and bone health in older men and women: The Rancho Bernardo Study. Osteoporos. Int. 2007, 18, 1337–1344. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, H.; Oh, H.J.; Choi, H.; Choi, W.H.; Lim, S.K.; Kim, J.G. The association between bone mineral density and metabolic syndrome: A Korean population-based study. J. Bone Miner. Metab. 2013, 31, 571–578. [Google Scholar] [CrossRef] [PubMed]
- Kim, T.; Park, S.; Pak, Y.S.; Lee, S.; Lee, E.H. Association between metabolic syndrome and bone mineral density in Korea: The Fourth Korea National Health and Nutrition Examination Survey (KNHANES IV), 2008. J. Bone Miner. Metab. 2013, 31, 652–662. [Google Scholar] [CrossRef]
- Lin, H.H.; Huang, C.Y.; Hwang, L.C. Association between metabolic syndrome and osteoporosis in Taiwanese middle-aged and elderly participants. Arch. Osteoporos. 2018, 13, 48. [Google Scholar] [CrossRef] [Green Version]
- Loke, S.S.; Chang, H.W.; Li, W.C. Association between metabolic syndrome and bone mineral density in a Taiwanese elderly population. J. Bone Miner. Metab. 2018, 36, 200–208. [Google Scholar] [CrossRef]
- Wu, Y.; Li, H.; Tao, X.; Fan, Y.; Gao, Q.; Yang, J. Optimised anthropometric indices as predictive screening tools for metabolic syndrome in adults: A cross-sectional study. BMJ Open 2021, 11, e043952. [Google Scholar] [CrossRef]
- Adejumo, E.N.; Adejumo, A.O.; Azenabor, A.; Ekun, A.O.; Enitan, S.S.; Adebola, O.K.; Ogundahunsi, O.A. Anthropometric parameter that best predict metabolic syndrome in South west Nigeria. Diabetes Metab. Syndr. 2019, 13, 48–54. [Google Scholar] [CrossRef]
- Lin, I.T.; Lee, M.Y.; Wang, C.W.; Wu, D.W.; Chen, S.C. Gender Differences in the Relationships among Metabolic Syndrome and Various Obesity-Related Indices with Nonalcoholic Fatty Liver Disease in a Taiwanese Population. Int. J. Environ. Res. Public Health 2021, 18, 857. [Google Scholar]
- Guo, X.; Ding, Q.; Liang, M. Evaluation of Eight Anthropometric Indices for Identification of Metabolic Syndrome in Adults with Diabetes. Diabetes Metab. Syndr. Obes. 2021, 14, 1431–1443. [Google Scholar] [CrossRef]
- Isomaa, B.; Henricsson, M.; Almgren, P.; Tuomi, T.; Taskinen, M.R.; Groop, L. The metabolic syndrome influences the risk of chronic complications in patients with type II diabetes. Diabetologia 2001, 44, 1148–1154. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tan, C.E.; Ma, S.; Wai, D.; Chew, S.K.; Tai, E.S. Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care 2004, 27, 1182–1186. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Guerrero-Romero, F.; Rodríguez-Morán, M. Abdominal volume index. An anthropometry-based index for estimation of obesity is strongly related to impaired glucose tolerance and type 2 diabetes mellitus. Arch. Med. Res. 2003, 34, 428–432. [Google Scholar] [CrossRef]
- Thomas, D.M.; Bredlau, C.; Bosy-Westphal, A.; Mueller, M.; Shen, W.; Gallagher, D.; Maeda, Y.; McDougall, A.; Peterson, C.M.; Ravussin, E.; et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity 2013, 21, 2264–2271. [Google Scholar] [CrossRef] [Green Version]
- Valdez, R. A simple model-based index of abdominal adiposity. J. Clin. Epidemiol. 1991, 44, 955–956. [Google Scholar] [CrossRef]
- Bergman, R.N.; Stefanovski, D.; Buchanan, T.A.; Sumner, A.E.; Reynolds, J.C.; Sebring, N.G.; Xiang, A.H.; Watanabe, R.M. A better index of body adiposity. Obesity 2011, 19, 1083–1089. [Google Scholar] [CrossRef]
- Amato, M.C.; Giordano, C.; Galia, M.; Criscimanna, A.; Vitabile, S.; Midiri, M.; Galluzzo, A.; for the AlkaMeSy Study Group. Visceral Adiposity Index: A reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care 2010, 33, 920–922. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kahn, H.S. The “lipid accumulation product” performs better than the body mass index for recognizing cardiovascular risk: A population-based comparison. BMC Cardiovasc. Disord. 2005, 5, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chin, K.Y.; Wong, S.K.; Ekeuku, S.O.; Pang, K.L. Relationship Between Metabolic Syndrome and Bone Health—An Evaluation of Epidemiological Studies and Mechanisms Involved. Diabetes Metab. Syndr. Obes. 2020, 13, 3667–3690. [Google Scholar] [CrossRef]
- Vu, J.P.; Larauche, M.; Flores, M.; Luong, L.; Norris, J.; Oh, S.; Liang, L.J.; Waschek, J.; Pisegna, J.R.; Germano, P.M. Regulation of Appetite, Body Composition, and Metabolic Hormones by Vasoactive Intestinal Polypeptide (VIP). J. Mol. Neurosci. 2015, 56, 377–387. [Google Scholar] [CrossRef] [Green Version]
- Farkhondeh, T.; Llorens, S.; Pourbagher-Shahri, A.M.; Ashrafizadeh, M.; Talebi, M.; Shakibaei, M.; Samarghandian, S. An Overview of the Role of Adipokines in Cardiometabolic Diseases. Molecules 2020, 25, 5218. [Google Scholar] [CrossRef] [PubMed]
- Weiner, J.; Zieger, K.; Pippel, J.; Heiker, J.T. Molecular Mechanisms of Vaspin Action—From Adipose Tissue to Skin and Bone, from Blood Vessels to the Brain. Adv. Exp. Med. Biol. 2019, 1111, 159–188. [Google Scholar] [PubMed] [Green Version]
- Rao, S.S.; Hu, Y.; Xie, P.L.; Cao, J.; Wang, Z.X.; Liu, J.H.; Yin, H.; Huang, J.; Tan, Y.J.; Luo, J.; et al. Omentin-1 prevents inflammation-induced osteoporosis by downregulating the pro-inflammatory cytokines. Bone Res. 2018, 6, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Nakanishi, K.; Shishido, K.; Kumata, C.; Ito, K.; Nakashima, Y.; Wakasa, M. Bone density of the femoral neck in patients on maintenance dialysis. PLoS ONE 2018, 13, e0197965. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Son, J.-S.; Koh, H.-M.; Park, J.-K. Relationship between Triglyceride and Bone Mineral Density in Healthy Korean Men. Korean J. Health Promot. 2015, 15, 115. [Google Scholar] [CrossRef] [Green Version]
- Mirzababaei, A.; Mirzaei, K.; Khorrami-Nezhad, L.; Maghbooli, Z.; Keshavarz, S.A. Metabolically healthy/unhealthy components may modify bone mineral density in obese people. Arch. Osteoporos. 2017, 12, 95. [Google Scholar] [CrossRef] [PubMed]
- Hirasawa, A.; Makita, K.; Akahane, T.; Yamagami, W.; Makabe, T.; Yokota, M.; Horiba, Y.; Ogawa, M.; Yanamoto, S.; Deshimaru, R.; et al. Osteoporosis is less frequent in endometrial cancer survivors with hypertriglyceridemia. Jpn. J. Clin. Oncol. 2015, 45, 127–131. [Google Scholar] [CrossRef] [Green Version]
- Chi, J.H.; Shin, M.S.; Lee, B.J. Identification of hypertriglyceridemia based on bone density, body fat mass, and anthropometry in a Korean population. BMC Cardiovasc. Disord. 2019, 19, 66. [Google Scholar] [CrossRef] [Green Version]
- Jiang, J.; Qiu, P.; Wang, Y.; Zhao, C.; Fan, S.; Lin, X. Association between serum high-density lipoprotein cholesterol and bone health in the general population: A large and multicenter study. Arch. Osteoporos. 2019, 14, 36. [Google Scholar] [CrossRef]
- Ilic, K.; Obradovic, N.; Vujasinovic-Stupar, N. The relationship among hypertension, antihypertensive medications, and osteoporosis: A narrative review. Calcif. Tissue Int. 2013, 92, 217–227. [Google Scholar] [CrossRef]
- Hanley, D.A.; Brown, J.P.; Tenenhouse, A.; Olszynski, W.P.; Ioannidis, G.; Berger, C.; Prior, J.C.; Pickard, L.; Murray, T.M.; Anastassiades, T.; et al. Associations among disease conditions, bone mineral density, and prevalent vertebral deformities in men and women 50 years of age and older: Cross-sectional results from the Canadian Multicentre Osteoporosis Study. J. Bone Miner. Res. 2003, 18, 784–790. [Google Scholar] [CrossRef] [PubMed]
- Tseng, Y.H.; Huang, K.C.; Liu, M.L.; Shu, W.T.; Sheu, W.H. Association between metabolic syndrome (MS) and bone mineral loss: A cross-sectional study in Puli Township in Taiwan. Arch. Gerontol. Geriatr. 2009, 49 (Suppl. 2), S37–S40. [Google Scholar] [CrossRef]
- Yang, S.; Nguyen, N.D.; Center, J.R.; Eisman, J.A.; Nguyen, T.V. Association between hypertension and fragility fracture: A longitudinal study. Osteoporos. Int. 2014, 25, 97–103. [Google Scholar] [CrossRef] [PubMed]
- Dogan, A.; Nakipoglu-Yuzer, G.F.; Yildizgoren, M.T.; Ozgirgin, N. Is age or the body mass index (BMI) more determinant of the bone mineral density (BMD) in geriatric women and men? Arch. Gerontol. Geriatr. 2010, 51, 338–341. [Google Scholar] [CrossRef]
- Kang, D.; Liu, Z.; Wang, Y.; Zhang, H.; Feng, X.; Cao, W.; Wang, P. Relationship of body composition with bone mineral density in northern Chinese men by body mass index levels. J. Endocrinol. Investig. 2014, 37, 359–367. [Google Scholar] [CrossRef] [PubMed]
- Kumar, A.; Sharma, A.K.; Mittal, S.; Kumar, G. The Relationship Between Body Mass Index and Bone Mineral Density in Premenopausal and Postmenopausal North Indian Women. J. Obstet. Gynaecol. India 2016, 66, 52–56. [Google Scholar] [CrossRef] [Green Version]
- Reid, I.R.; Baldock, P.A.; Cornish, J. Effects of Leptin on the Skeleton. Endocr. Rev. 2018, 39, 938–959. [Google Scholar] [CrossRef] [PubMed]
- Hamrick, M.W.; Della-Fera, M.A.; Choi, Y.H.; Pennington, C.; Hartzell, D.; Baile, C.A. Leptin treatment induces loss of bone marrow adipocytes and increases bone formation in leptin-deficient ob/ob mice. J. Bone Miner. Res. 2005, 20, 994–1001. [Google Scholar] [CrossRef] [PubMed]
- Chiu, T.H.; Huang, Y.C.; Chiu, H.; Wu, P.Y.; Chiou, H.C.; Huang, J.C.; Chen, S.C. Comparison of Various Obesity-Related Indices for Identification of Metabolic Syndrome: A Population-Based Study from Taiwan Biobank. Diagnostics 2020, 10, 1081. [Google Scholar] [CrossRef]
- Deng, G.; Yin, L.; Li, K.; Hu, B.; Cheng, X.; Wang, L.; Zhang, Y.; Xu, L.; Xu, S.; Zhu, L.; et al. Relationships between anthropometric adiposity indexes and bone mineral density in a cross-sectional Chinese study. Spine J. 2021, 21, 332–342. [Google Scholar] [CrossRef]
- Liu, B.; Liu, B.; Wu, G.; Yin, F. Relationship between body-roundness index and metabolic syndrome in type 2 diabetes. Diabetes Metab. Syndr. Obes. 2019, 12, 931–935. [Google Scholar] [CrossRef] [Green Version]
- Mantzoros, C.S.; Evagelopoulou, K.; Georgiadis, E.I.; Katsilambros, N. Conicity index as a predictor of blood pressure levels, insulin and triglyceride concentrations of healthy premenopausal women. Horm. Metab. Res. 1996, 28, 32–34. [Google Scholar] [CrossRef] [PubMed]
- Xiao, H.; Xiong, C.; Shao, X.; Gao, P.; Chen, H.; Ning, J.; Chen, Y.; Zou, Z.; Hong, G.; Li, X.; et al. Visceral Adiposity Index and Chronic Kidney Disease in a Non-Diabetic Population: A Cross-Sectional Study. Diabetes Metab. Syndr. Obes. 2020, 13, 257–265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Adu, E.A.; Obirikorang, C.; Acheampong, E.; Kwakye, A.S.; Fokuoh, F.; Obirikorang, Y.; Anto, E.O.; Batu, E.N.; Amoah, B.Y.; Ansong, P.N.; et al. Lipid accumulation product (LAP) index as a potential risk assessment for cardiovascular risk stratification among type II diabetes mellitus in a Ghanaian population: A cross-sectional study. Cogent Med. 2019, 6, 1639880. [Google Scholar] [CrossRef]
- Bramania, P.; Ruggajo, P.; Bramania, R.; Mahmoud, M.; Furia, F. Nutritional Status of Patients on Maintenance Hemodialysis at Muhimbili National Hospital in Dar es Salaam, Tanzania: A Cross-Sectional Study. J. Nutr. Metab. 2021, 2021, 6672185. [Google Scholar] [CrossRef]
- Saunders, J.; Smith, T. Malnutrition: Causes and consequences. Clin. Med. 2010, 10, 624–627. [Google Scholar] [CrossRef]
Characteristics | All Patients (n = 164) | MetS (−) (n = 63) | MetS (+) (n = 101) | p |
---|---|---|---|---|
Age (years) | 60.1 ± 10.6 | 58.9 ± 11.2 | 60.9 ± 10.2 | 0.236 |
Men (%) | 54.9 | 52.4 | 56.4 | 0.612 |
Diabetes (%) | 51.8 | 23.8 | 69.3 | <0.001 |
Hypertension (%) | 92.7 | 82.5 | 99.0 | <0.001 |
Duration of HD (years) | 6.9 (3.3–13.1) | 10.5 (6.0–16.6) | 4.8 (2.5–9.6) | <0.001 |
Body height (cm) | 161.6 ± 8.2 | 160.9 ± 8.3 | 162.0 ± 8.1 | 0.385 |
Body weight (kg) | 62.6 ± 12.0 | 56.0 ± 9.7 | 66.8 ± 11.6 | <0.001 |
Waist circumference (cm) | 87.2 ± 10.9 | 79.3 ± 10.0 | 92.0 ± 8.3 | <0.001 |
Hip circumference (cm) | 92.8 ± 7.6 | 89.2 ± 6.5 | 95.0 ± 7.5 | <0.001 |
DXA Parameters | ||||
Lumbar spine T-score | −1.21 ± 1.69 | −1.89 ± 1.60 | −0.84 ± 1.63 | <0.001 |
Femoral neck T-score | −2.29 ± 1.18 | −2.48 ± 0.95 | −2.18 ± 1.28 | 0.139 |
Total hip T-score | −1.79 ± 1.28 | −2.13 ± 1.00 | −1.60 ± 1.38 | 0.009 |
Laboratory parameters | ||||
Fasting glucose (mg/dL) | 111.4 ± 43.3 | 90.8 ± 21.1 | 124.3 ± 48.4 | <0.001 |
Albumin (g/dL) | 3.9 ± 0.3 | 3.9 ± 0.2 | 3.9 ± 0.3 | 0.091 |
Hemoglobin (g/dL) | 10.3 ± 1.3 | 10.3 ± 1.5 | 10.4 ± 1.4 | 0.689 |
Triglycerides (mg/dL) | 111 (82.3–164.8) | 81 (66–102) | 146 (104.5–205) | <0.001 |
Total cholesterol (mg/dL) | 172.2 ± 42.6 | 168.8 ± 35.1 | 174.3 ± 46.7 | 0.423 |
HDL-cholesterol (mg/dL) | 44.0 ± 12.9 | 53.6 ± 13.2 | 38.0 ± 8.4 | <0.001 |
LDL-cholesterol (mg/dL) | 89.4 ± 29.6 | 84.4 ± 27.1 | 92.6 ± 30.8 | 0.086 |
CaXP product (mg2/dL2) | 41.4 ± 10.1 | 41.3 ± 9.7 | 41.4 ± 10.4 | 0.921 |
PTH (pg/mL) | 301.1 (159.4–507.7) | 284.6 (158.4–506.4) | 319.2 (159.7–513.1) | 0.362 |
MetS component | ||||
Abdominal obesity (%) | 54.9 | 19.0 | 77.2 | <0.001 |
Hypertriglyceridemia (%) | 32.9 | 6.3 | 49.5 | <0.001 |
Low HDL-cholesterol (%) | 57.9 | 22.2 | 80.2 | <0.001 |
Hyperglycemia (%) | 61.0 | 30.2 | 80.2 | <0.001 |
High blood pressure (%) | 92.7 | 82.5 | 99.0 | <0.001 |
Obesity-related indices | ||||
BMI (kg/m2) | 23.9 ± 4.0 | 21.6 ± 3.1 | 25.4 ± 3.8 | <0.001 |
WHR | 0.94 ± 0.08 | 0.89 ± 0.07 | 0.97 ± 0.06 | <0.001 |
WHtR | 0.54 ± 0.07 | 0.49 ± 0.06 | 0.57 ± 0.05 | <0.001 |
AVI | 15.5 ± 3.8 | 12.8 ± 3.2 | 17.1 ± 3.1 | <0.001 |
BRI | 4.2 ± 1.4 | 3.3 ± 1.2 | 4.8 ± 1.2 | <0.001 |
CI | 1.29 ± 0.09 | 1.23 ± 0.09 | 1.32 ± 0.07 | <0.001 |
BAI | 27.4 ± 4.6 | 26.0 ± 4.1 | 28.2 ± 4.7 | 0.002 |
VAI | 6.4 ± 7.1 | 2.9 ± 2.2 | 8.4 ± 8.2 | <0.001 |
LAP | 46.6 ± 53.0 | 19.0 ± 14.9 | 63.2 ± 60.3 | <0.001 |
Parameters | Lumbar Spine T-Score | Femoral Neck T-Score | Total Hip T-Score | |||
---|---|---|---|---|---|---|
Coefficient β (95% CI) | p | Coefficient β (95% CI) | p | Coefficient β (95% CI) | p | |
MetS | 1.116 (0.543, 1.689) | <0.001 | 0.374 (−0.018, 0.766) | 0.061 | 0.503 (0.076, 0.931) | 0.021 |
MetS component | ||||||
Abdominal obesity | 1.091 (0.514, 1.668) | <0.001 | 0.679 (0.299, 1.058) | 0.001 | 0.678 (0.258, 1.099) | 0.002 |
Hypertriglyceridemia | 0.674 (0.083, 1.264) | 0.026 | 0.314 (−0.092, 0.719) | 0.128 | 0.502 (0.061, 0.942) | 0.026 |
Low HDL-cholesterol | 0.838 (0.313, 1.363) | 0.002 | 0.537 (0.184, 0.890) | 0.003 | 0.658 (0.274, 1.043) | 0.001 |
Hyperglycemia | 0.782 (0.225, 1.339) | 0.006 | 0.020 (−0.363, 0.402) | 0.918 | 0.172 (−0.247, 0.591) | 0.418 |
High blood pressure | 0.203 (−0.907, 1.314) | 0.718 | −0.593 (−1.342, 0.156) | 0.119 | −0.456 (−1.281, 0.370) | 0.277 |
Obesity-Related Indices | Lumbar Spine T-Score | Femoral Neck T-Score | Total Hip T-Score | |||
---|---|---|---|---|---|---|
Coefficient β (95% CI) | p | Coefficient β (95% CI) | p | Coefficient β (95% CI) | p | |
BMI (per 1 kg/m2) * | 0.120 (0.029, 0.211) | 0.010 | 0.109 (0.041, 0.177) | 0.002 | 0.105 (0.029, 0.181) | 0.007 |
WHR (per 0.1) * | 0.419 (−0.131, 0.968) | 0.133 | 0.335 (−0.075, 0.745) | 0.108 | 0.323 (−0.128, 0.773) | 0.158 |
WHtR (per 0.1) * | 0.929 (0.304, 1.555) | 0.004 | 0.605 (0.128, 1.082) | 0.014 | 0.561 (0.034, 1.089) | 0.037 |
AVI (per 1) * | 0.192 (0.087, 0.297) | 0.001 | 0.138 (0.058, 0.219) | 0.001 | 0.119 (0.028, 0.209) | 0.011 |
BRI (per 1) * | 0.411 (0.129, 0.692) | 0.005 | 0.267 (0.052, 0.481) | 0.016 | 0.245 (0.008, 0.483) | 0.043 |
CI (per 0.1) * | 0.557 (0.034, 1.080) | 0.037 | 0.215 (−0.184, 0.613) | 0.287 | 0.168 (−0.269, 0.606) | 0.445 |
BAI (per 1) * | 0.063 (−0.017, 0.143) | 0.121 | 0.030 (−0.029, 0.090) | 0.314 | 0.030 (−0.036, 0.095) | 0.367 |
VAI (per 1) † | 0.090 (0.019, 0.161) | 0.013 | 0.019 (−0.034, 0.072) | 0.485 | 0.031 (−0.027, 0.088) | 0.291 |
LAP (per 10) # | 0.148 (0.020, 0.277) | 0.024 | 0.057 (−0.043, 0.158) | 0.257 | 0.081 (−0.028, 0.191) | 0.142 |
Obesity-Related Indices | Lumbar Spine T-Score | Femoral Neck T-Score | Total Hip T-Score | |||
---|---|---|---|---|---|---|
Coefficient β (95% CI) | p | Coefficient β (95% CI) | p | Coefficient β (95% CI) | p | |
BMI (per 1 kg/m2) * | 0.041 (−0.131, 0.213) | 0.631 | 0.059 (−0.031, 0.149) | 0.194 | 0.051 (−0.047, 0.148) | 0.299 |
WHR (per 0.1) * | 0.226 (−0.626, 1.077) | 0.592 | 0.410 (−0.060, 0.879) | 0.085 | 0.321 (−0.173, 0.814) | 0.195 |
WHtR (per 0.1) * | −0.032 (−0.944, 0.879) | 0.943 | 0.134 (−0.377, 0.646) | 0.597 | 0.089 (−0.447, 0.625) | 0.738 |
AVI (per 1) * | 0.084 (−0.082, 0.250) | 0.312 | 0.083 (−0.010, 0.175) | 0.078 | 0.066 (−0.031, 0.163) | 0.177 |
BRI (per 1) * | −0.036 (−0.505, 0.432) | 0.876 | 0.059 (−0.205, 0.323) | 0.652 | 0.035 (−0.241, 0.312) | 0.797 |
CI (per 0.1) * | 0.215 (−0.447, 0.876) | 0.513 | 0.115 (−0.274, 0.504) | 0.553 | 0.061 (−0.347, 0.469) | 0.764 |
BAI (per 1) * | −0.155 (−0.321, 0.011) | 0.066 | −0.065 (−0.150, 0.021) | 0.136 | −0.052 (−0.142, 0.037) | 0.243 |
VAI (per 1) † | 0.056 (−0.195, 0.308) | 0.651 | −0.044 (−0.183, 0.095) | 0.525 | −0.036 (−0.181, 0.110) | 0.620 |
LAP (per 10) # | 0.184 (−0.213, 0.582) | 0.352 | 0.046 (−0.185, 0.277) | 0.687 | 0.003 (−0.239, 0.246) | 0.977 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wung, C.-H.; Chung, C.-Y.; Wu, P.-Y.; Huang, J.-C.; Tsai, Y.-C.; Chen, S.-C.; Chiu, Y.-W.; Chang, J.-M. Associations between Metabolic Syndrome and Obesity-Related Indices and Bone Mineral Density T-Score in Hemodialysis Patients. J. Pers. Med. 2021, 11, 775. https://doi.org/10.3390/jpm11080775
Wung C-H, Chung C-Y, Wu P-Y, Huang J-C, Tsai Y-C, Chen S-C, Chiu Y-W, Chang J-M. Associations between Metabolic Syndrome and Obesity-Related Indices and Bone Mineral Density T-Score in Hemodialysis Patients. Journal of Personalized Medicine. 2021; 11(8):775. https://doi.org/10.3390/jpm11080775
Chicago/Turabian StyleWung, Chih-Hsuan, Cheng-Yin Chung, Pei-Yu Wu, Jiun-Chi Huang, Yi-Chun Tsai, Szu-Chia Chen, Yi-Wen Chiu, and Jer-Ming Chang. 2021. "Associations between Metabolic Syndrome and Obesity-Related Indices and Bone Mineral Density T-Score in Hemodialysis Patients" Journal of Personalized Medicine 11, no. 8: 775. https://doi.org/10.3390/jpm11080775