A Nonlinear Relation between Body Mass Index and Long-Term Poststroke Functional Outcome—The Importance of Insulin Resistance, Inflammation, and Insulin-like Growth Factor-Binding Protein-1
Abstract
:1. Introduction
2. Results
2.1. Baseline Characteristics and Correlations
2.2. BMI and Poststroke Functional Outcome
2.3. The Relation between BMI, HOMA-IR, and s-IGFBP-1 and Poststroke Functional Outcome
2.4. Poor Functional Outcome in IGFBP-1, HOMA-IR, and BMI Categories
3. Discussion
3.1. Nonlinear Associations between BMI and Poor Functional Outcome—Relations with IGFBP-1 and Insulin Resistance
3.2. Different Attenuation for Poor Functional Outcome in the Normal-Weight and the Obese
3.3. Impact and Additive Effects of IGFBP-1 and HOMA-IR in the Different BMI Categories
3.4. Effect of Follow-Up Time
3.5. Strengths and Limitations
3.6. The Obesity Paradox and Other Possible Mechanisms
4. Materials and Methods
4.1. Study Population
4.2. Stroke Severity and Functional Outcome
4.3. Blood Sampling and Protein Measurement
4.4. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021, 20, 795–820. [Google Scholar] [CrossRef] [PubMed]
- Cassidy, J.M.; Cramer, S.C. Spontaneous and Therapeutic-Induced Mechanisms of Functional Recovery After Stroke. Transl. Stroke Res. 2017, 8, 33–46. [Google Scholar] [CrossRef] [PubMed]
- Strazzullo, P.; D’Elia, L.; Cairella, G.; Garbagnati, F.; Cappuccio, F.P.; Scalfi, L. Excess body weight and incidence of stroke: Meta-analysis of prospective studies with 2 million participants. Stroke 2010, 41, e418–e426. [Google Scholar] [CrossRef] [PubMed]
- Flegal, K.M.; Ioannidis, J.P.A. The Obesity Paradox: A Misleading Term That Should Be Abandoned. Obesity 2018, 26, 629–630. [Google Scholar] [CrossRef] [PubMed]
- Wakisaka, K.; Matsuo, R.; Matsumoto, K.; Nohara, Y.; Irie, F.; Wakisaka, Y.; Ago, T.; Nakashima, N.; Kamouchi, M.; Kitazono, T. Non-linear association between body weight and functional outcome after acute ischemic stroke. Sci. Rep. 2023, 13, 8697. [Google Scholar] [CrossRef]
- Forlivesi, S.; Cappellari, M.; Bonetti, B. Obesity paradox and stroke: A narrative review. Eat. Weight. Disord. 2021, 26, 417–423. [Google Scholar] [CrossRef] [PubMed]
- Olsen, T.S.; Dehlendorff, C.; Petersen, H.G.; Andersen, K.K. Body mass index and poststroke mortality. Neuroepidemiology 2008, 30, 93–100. [Google Scholar] [CrossRef] [PubMed]
- Aparicio, H.J.; Himali, J.J.; Beiser, A.S.; Davis-Plourde, K.L.; Vasan, R.S.; Kase, C.S.; Wolf, P.A.; Seshadri, S. Overweight, Obesity, and Survival After Stroke in the Framingham Heart Study. J. Am. Heart Assoc. 2017, 6, e004721. [Google Scholar] [CrossRef] [PubMed]
- 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]
- Takhttavous, A.; Saberi-Karimian, M.; Hafezi, S.G.; Esmaily, H.; Hosseini, M.; Ferns, G.A.; Amirfakhrian, E.; Ghamsary, M.; Ghayour-Mobarhan, M.; Alinezhad-Namaghi, M. Predicting the 10-year incidence of dyslipidemia based on novel anthropometric indices, using data mining. Lipids Health Dis. 2024, 23, 33. [Google Scholar] [CrossRef]
- Chang, V.W.; Langa, K.M.; Weir, D.; Iwashyna, T.J. The obesity paradox and incident cardiovascular disease: A population-based study. PLoS ONE 2017, 12, e0188636. [Google Scholar] [CrossRef] [PubMed]
- Scherbakov, N.; Dirnagl, U.; Doehner, W. Body weight after stroke: Lessons from the obesity paradox. Stroke 2011, 42, 3646–3650. [Google Scholar] [CrossRef] [PubMed]
- Mohamed-Ali, V.; Goodrick, S.; Bulmer, K.; Holly, J.M.; Yudkin, J.S.; Coppack, S.W. Production of soluble tumor necrosis factor receptors by human subcutaneous adipose tissue in vivo. Am. J. Physiol. 1999, 277, E971–E975. [Google Scholar] [CrossRef] [PubMed]
- Weber, M.A.; Neutel, J.M.; Smith, D.H. Contrasting clinical properties and exercise responses in obese and lean hypertensive patients. J. Am. Coll. Cardiol. 2001, 37, 169–174. [Google Scholar] [CrossRef] [PubMed]
- Chang, Y.; Kim, C.K.; Kim, M.K.; Seo, W.K.; Oh, K. Insulin resistance is associated with poor functional outcome after acute ischemic stroke in non-diabetic patients. Sci. Rep. 2021, 11, 1229. [Google Scholar] [CrossRef] [PubMed]
- Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis Model Assessment—Insulin Resistance and Beta-Cell Function from Fasting Plasma-Glucose and Insulin Concentrations in Man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef] [PubMed]
- Hanley, A.J.; Williams, K.; Stern, M.P.; Haffner, S.M. Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease: The San Antonio Heart Study. Diabetes Care 2002, 25, 1177–1184. [Google Scholar] [CrossRef] [PubMed]
- Li, S.; Yin, C.; Zhao, W.; Zhu, H.; Xu, D.; Xu, Q.; Jiao, Y.; Wang, X.; Qiao, H. Homeostasis model assessment of insulin resistance in relation to the poor functional outcomes in nondiabetic patients with ischemic stroke. Biosci. Rep. 2018, 38, BSR20180330. [Google Scholar] [CrossRef]
- Banait, T.; Wanjari, A.; Danade, V.; Banait, S.; Jain, J. Role of High-Sensitivity C-reactive Protein (Hs-CRP) in Non-communicable Diseases: A Review. Cureus 2022, 14, e30225. [Google Scholar] [CrossRef] [PubMed]
- Singh, P.; Hoffmann, M.; Wolk, R.; Shamsuzzaman, A.S.; Somers, V.K. Leptin induces C-reactive protein expression in vascular endothelial cells. Arterioscler. Thromb. Vasc. Biol. 2007, 27, e302–e307. [Google Scholar] [CrossRef] [PubMed]
- Andersson, J.; Johansson, L.; Ladenvall, P.; Wiklund, P.G.; Stegmayr, B.; Jern, C.; Boman, K. C-reactive protein is a determinant of first-ever stroke: Prospective nested case-referent study. Cerebrovasc. Dis. 2009, 27, 544–551. [Google Scholar] [CrossRef] [PubMed]
- Wang, A.; Liu, J.; Li, C.; Gao, J.; Li, X.; Chen, S.; Wu, S.; Ding, H.; Fan, H.; Hou, S. Cumulative Exposure to High-Sensitivity C-Reactive Protein Predicts the Risk of Cardiovascular Disease. J. Am. Heart Assoc. 2017, 6, e005610. [Google Scholar] [CrossRef] [PubMed]
- Coveney, S.; Murphy, S.; Belton, O.; Cassidy, T.; Crowe, M.; Dolan, E.; de Gaetano, M.; Harbison, J.; Horgan, G.; Marnane, M.; et al. Inflammatory cytokines, high-sensitivity C-reactive protein, and risk of one-year vascular events, death, and poor functional outcome after stroke and transient ischemic attack. Int. J. Stroke 2022, 17, 163–171. [Google Scholar] [CrossRef] [PubMed]
- Aberg, N.D.; Brywe, K.G.; Isgaard, J. Aspects of growth hormone and insulin-like growth factor-I related to neuroprotection, regeneration, and functional plasticity in the adult brain. Sci. World J. 2006, 6, 53–80. [Google Scholar] [CrossRef]
- Aberg, D.; Gadd, G.; Jood, K.; Redfors, P.; Stanne, T.M.; Isgaard, J.; Blennow, K.; Zetterberg, H.; Jern, C.; Aberg, N.D.; et al. Serum IGFBP-1 Concentration as a Predictor of Outcome after Ischemic Stroke-A Prospective Observational Study. Int. J. Mol. Sci. 2023, 24, 9120. [Google Scholar] [CrossRef] [PubMed]
- Heald, A.H.; Cruickshank, J.K.; Riste, L.K.; Cade, J.E.; Anderson, S.; Greenhalgh, A.; Sampayo, J.; Taylor, W.; Fraser, W.; White, A.; et al. Close relation of fasting insulin-like growth factor binding protein-1 (IGFBP-1) with glucose tolerance and cardiovascular risk in two populations. Diabetologia 2001, 44, 333–339. [Google Scholar] [CrossRef] [PubMed]
- Rajpathak, S.N.; McGinn, A.P.; Strickler, H.D.; Rohan, T.E.; Pollak, M.; Cappola, A.R.; Kuller, L.; Xue, X.; Newman, A.B.; Strotmeyer, E.S.; et al. Insulin-like growth factor-(IGF)-axis, inflammation, and glucose intolerance among older adults. Growth Horm. IGF Res. 2008, 18, 166–173. [Google Scholar] [CrossRef] [PubMed]
- Janssen, J.A.; Stolk, R.P.; Pols, H.A.; Grobbee, D.E.; Lamberts, S.W. Serum total IGF-I, free IGF-I, and IGFB-1 levels in an elderly population: Relation to cardiovascular risk factors and disease. Arterioscler. Thromb. Vasc. Biol. 1998, 18, 277–282. [Google Scholar] [CrossRef] [PubMed]
- Yeap, B.B.; Chubb, S.A.; McCaul, K.A.; Ho, K.K.; Hankey, G.J.; Norman, P.E.; Flicker, L. Associations of IGF1 and IGFBPs 1 and 3 with all-cause and cardiovascular mortality in older men: The Health In Men Study. Eur. J. Endocrinol. 2011, 164, 715–723. [Google Scholar] [CrossRef]
- Ritsinger, V.; Brismar, K.; Mellbin, L.; Nasman, P.; Ryden, L.; Soderberg, S.; Norhammar, A. Elevated levels of insulin-like growth factor-binding protein 1 predict outcome after acute myocardial infarction: A long-term follow-up of the glucose tolerance in patients with acute myocardial infarction (GAMI) cohort. Diab Vasc. Dis. Res. 2018, 15, 387–395. [Google Scholar] [CrossRef] [PubMed]
- Kaplan, R.C.; McGinn, A.P.; Pollak, M.N.; Kuller, L.; Strickler, H.D.; Rohan, T.E.; Xue, X.; Kritchevsky, S.B.; Newman, A.B.; Psaty, B.M. Total insulinlike growth factor 1 and insulinlike growth factor binding protein levels, functional status, and mortality in older adults. J. Am. Geriatr. Soc. 2008, 56, 652–660. [Google Scholar] [CrossRef] [PubMed]
- Flegal, K.M.; Kit, B.K.; Orpana, H.; Graubard, B.I. Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. JAMA 2013, 309, 71–82. [Google Scholar] [CrossRef] [PubMed]
- Xu, J.; Wang, A.; Meng, X.; Jing, J.; Wang, Y.; Wang, Y.; Investigators for ACROSS-China. Obesity-Stroke Paradox Exists in Insulin-Resistant Patients But Not Insulin Sensitive Patients. Stroke 2019, 50, 1423–1429. [Google Scholar] [CrossRef]
- Aberg, D.; Aberg, N.D.; Jood, K.; Holmegaard, L.; Redfors, P.; Blomstrand, C.; Isgaard, J.; Jern, C.; Svensson, J. Homeostasis model assessment of insulin resistance and outcome of ischemic stroke in non-diabetic patients—A prospective observational study. BMC Neurol. 2019, 19, 177. [Google Scholar] [CrossRef] [PubMed]
- Bell, C.L.; Rantanen, T.; Chen, R.; Davis, J.; Petrovitch, H.; Ross, G.W.; Masaki, K. Prestroke weight loss is associated with poststroke mortality among men in the Honolulu-Asia Aging Study. Arch. Phys. Med. Rehabil. 2014, 95, 472–479. [Google Scholar] [CrossRef] [PubMed]
- Yoo, S.H.; Kim, J.S.; Kwon, S.U.; Yun, S.C.; Koh, J.Y.; Kang, D.W. Undernutrition as a predictor of poor clinical outcomes in acute ischemic stroke patients. Arch. Neurol. 2008, 65, 39–43. [Google Scholar] [CrossRef]
- Peduzzi, P.; Concato, J.; Kemper, E.; Holford, T.R.; Feinstein, A.R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 1996, 49, 1373–1379. [Google Scholar] [CrossRef]
- Kodama, K.; Tojjar, D.; Yamada, S.; Toda, K.; Patel, C.J.; Butte, A.J. Ethnic differences in the relationship between insulin sensitivity and insulin response: A systematic review and meta-analysis. Diabetes Care 2013, 36, 1789–1796. [Google Scholar] [CrossRef] [PubMed]
- Consultation, W.H.O.E. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004, 363, 157–163. [Google Scholar] [CrossRef] [PubMed]
- Wallace, T.M.; Levy, J.C.; Matthews, D.R. Use and abuse of HOMA modeling. Diabetes Care 2004, 27, 1487–1495. [Google Scholar] [CrossRef] [PubMed]
- Chen, A.Q.; Fang, Z.; Chen, X.L.; Yang, S.; Zhou, Y.F.; Mao, L.; Xia, Y.P.; Jin, H.J.; Li, Y.N.; You, M.F.; et al. Microglia-derived TNF-alpha mediates endothelial necroptosis aggravating blood brain-barrier disruption after ischemic stroke. Cell Death Dis. 2019, 10, 487. [Google Scholar] [CrossRef]
- Lin, S.Y.; Wang, Y.Y.; Chang, C.Y.; Wu, C.C.; Chen, W.Y.; Liao, S.L.; Chen, C.J. TNF-alpha Receptor Inhibitor Alleviates Metabolic and Inflammatory Changes in a Rat Model of Ischemic Stroke. Antioxidants 2021, 10, 851. [Google Scholar] [CrossRef] [PubMed]
- Bonetti, N.R.; Diaz-Canestro, C.; Liberale, L.; Crucet, M.; Akhmedov, A.; Merlini, M.; Reiner, M.F.; Gobbato, S.; Stivala, S.; Kollias, G.; et al. Tumour Necrosis Factor-alpha Inhibition Improves Stroke Outcome in a Mouse Model of Rheumatoid Arthritis. Sci. Rep. 2019, 9, 2173. [Google Scholar] [CrossRef] [PubMed]
- Cheung, Y.M.; Joham, A.; Marks, S.; Teede, H. The obesity paradox: An endocrine perspective. Intern. Med. J. 2017, 47, 727–733. [Google Scholar] [CrossRef] [PubMed]
- Jood, K.; Ladenvall, C.; Rosengren, A.; Blomstrand, C.; Jern, C. Family history in ischemic stroke before 70 years of age: The Sahlgrenska Academy Study on Ischemic Stroke. Stroke 2005, 36, 1383–1387. [Google Scholar] [CrossRef] [PubMed]
- Aberg, D.; Jood, K.; Blomstrand, C.; Jern, C.; Nilsson, M.; Isgaard, J.; Aberg, N.D. Serum IGF-I levels correlate to improvement of functional outcome after ischemic stroke. J. Clin. Endocrinol. Metab. 2011, 96, E1055–E1064. [Google Scholar] [CrossRef] [PubMed]
- Perna, S.; Peroni, G.; Faliva, M.A.; Bartolo, A.; Naso, M.; Miccono, A.; Rondanelli, M. Sarcopenia and sarcopenic obesity in comparison: Prevalence, metabolic profile, and key differences. A cross-sectional study in Italian hospitalized elderly. Aging Clin. Exp. Res. 2017, 29, 1249–1258. [Google Scholar] [CrossRef] [PubMed]
- World Health Organization. Physical status: The use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ. Tech. Rep. Ser. 1995, 854, 1–452. [Google Scholar]
- Aberg, N.D.; Gadd, G.; Aberg, D.; Hallgren, P.; Blomstrand, C.; Jood, K.; Nilsson, M.; Walker, F.R.; Svensson, J.; Jern, C.; et al. Relationship between Levels of Pre-Stroke Physical Activity and Post-Stroke Serum Insulin-Like Growth Factor I. Biomedicines 2020, 8, 52. [Google Scholar] [CrossRef] [PubMed]
- Gray, L.J.; Ali, M.; Lyden, P.D.; Bath, P.M.; Virtual International Stroke Trials Archive, C. Interconversion of the National Institutes of Health Stroke Scale and Scandinavian Stroke Scale in acute stroke. J. Stroke Cerebrovasc. Dis. 2009, 18, 466–468. [Google Scholar] [CrossRef] [PubMed]
- Cohen, J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed.; Routledge: London, UK, 1988. [Google Scholar] [CrossRef]
- Olsson, S.; Jood, K.; Blomstrand, C.; Jern, C. Genetic variation on chromosome 9p21 shows association with the ischaemic stroke subtype large-vessel disease in a Swedish sample aged≤ 70. Eur. J. Neurol. 2011, 18, 365–367. [Google Scholar] [CrossRef] [PubMed]
- Jood, K.; Ladenvall, P.; Tjarnlund-Wolf, A.; Ladenvall, C.; Andersson, M.; Nilsson, S.; Jern, C.; Blomstrand, C. Fibrinolytic gene polymorphism and ischemic stroke. Stroke 2005, 36, 2077–2081. [Google Scholar] [CrossRef] [PubMed]
- Aberg, N.D.; Aberg, D.; Jood, K.; Nilsson, M.; Blomstrand, C.; Kuhn, H.G.; Isgaard, J.; Jern, C.; Svensson, J. Altered levels of circulating insulin-like growth factor I (IGF-I) following ischemic stroke are associated with outcome—A prospective observational study. BMC Neurol. 2018, 18, 106. [Google Scholar] [CrossRef] [PubMed]
- Rankin, J. Cerebral vascular accidents in patients over the age of 60. II. Prognosis. Scott. Med. J. 1957, 2, 200–215. [Google Scholar] [CrossRef] [PubMed]
- Banks, J.L.; Marotta, C.A. Outcomes validity and reliability of the modified Rankin scale: Implications for stroke clinical trials: A literature review and synthesis. Stroke 2007, 38, 1091–1096. [Google Scholar] [CrossRef] [PubMed]
All | A. Normal-Weight | p | B. Overweight | p | C. Obese | p | |
---|---|---|---|---|---|---|---|
Variable | n | (BMI 18.5–25) | A vs. B | (BMI 25–30) | B vs. C | (BMI > 30) | A vs. C |
All patients (N, %) | 451 | 180 (100) | NA | 193 (100) | NA | 78 (100) | NA |
Females (N, %) | 163 | 74 (41.1) | 0.034 | 59 (30.6) | 0.212 | 30 (38.5) | 0.692 |
Males (N, %) | 288 | 106 (58.9) | 0.034 | 134 (69.4) | 0.212 | 48 (61.5) | 0.692 |
Age, years (95% CI) | 451 | 54.9 (53.2–56.6) | 0.042 | 57.9 (56.6–59.2) | 0.54 | 58.4 (56.4–60.4) | 0.048 |
BMI, kg/m2 (95% CI) | 451 | 22.8 (22.6–23.1) | <0.001 | 27.3 (27.1–27.5) | <0.001 | 33.7 (32.9–34.5) | <0.001 |
Hypertension (N, %) | 451 | 85 (47.2) | 0.001 | 123 (63.7) | 0.021 | 61 (78.2) | <0.001 |
Systolic BP, mmHg, mean (95% CI) | 442 | 143 (138–147) | 0.010 | 148 (145–152) | 0.586 | 150 (145–156) | 0.011 |
Diastolic BP, mmHg, mean (95% CI) | 441 | 82 (80–85) | 0.039 | 85 (83–87) | 0.696 | 86 (83–88) | 0.041 |
Smoking (N, %) | 451 | 77 (42.8) | 0.166 | 69 (35.8) | 0.326 | 23 (29.5) | 0.044 |
Diabetes (N, %) | 451 | 19 (10.6) | 0.003 | 42 (21.8) | 0.119 | 24 (30.8) | <0.001 |
LDL, mmol/L, mean (95% CI) | 388 | 3.17 (3.03–3.31) | 0.008 | 3.47 (3.32–3.62) | 0.632 | 3.40 (3.12–3.69) | 0.173 |
Imputed LDL, mmol/L, mean (95% CI) | 451 | 3.18 (3.06–3.31) | 0.007 | 3.45 (3.32–3.58) | 0.643 | 3.39 (3.16–3.62) | 0.123 |
hs-CRP, mg/L, mean (95% CI) # | 431 | 11.0 (7.39–14.6) | 0.551 | 7.78 (5.63–9.93) | 0.009 | 10.7 (6.95–14.4) | 0.006 |
Sedentary lifestyle (N/tot N, %) | 424 | 22 (12.9) | 0.668 | 26 (14.4) | <0.001 | 25 (34.2) | <0.001 |
Previous stroke (N/tot N, %) | 451 | 35 (19.4) | 0.953 | 38 (19.6) | 0.932 | 15 (19.2) | 0.968 |
NIHSS, mean (95% CI) | 451 | 5.20 (4.35–6.04) | 0.858 | 4.49 (3.81–5.18) | 0.043 | 5.35 (4.27–6.44) | 0.096 |
s-IGFBP1, µg/L, mean (95% CI) | 341 | 8.70 (7.14–10.3) | 0.017 | 7.07 (5.65–8.48) | 0.011 | 4.50 (3.49–5.51) | <0.001 |
Insulin, microU/L, mean (95% CI) | 430 | 12.8 (10.6–14.9) | <0.001 | 14.7 (13.0–16.4) | <0.001 | 23.6 (19.0–28.0) | <0.001 |
Glucose, nmol/L, mean (95% CI) | 427 | 5.92 (5.56–6.27) | <0.001 | 6.64 (6.26–7.01) | 0.309 | 6.75 (6.17–7.33) | <0.001 |
HOMA-IR, mean (95% CI) | 413 | 3.55 (2.86–4.24) | <0.001 | 4.69 (3.90–5.48) | <0.001 | 6.65 (5.43–7.88) | <0.001 |
Parameter | BMI | s-IGFBP-1 | HOMA-IR | hs-CRP |
---|---|---|---|---|
BMI, r (p) | NA | −0.24 (<0.001) | 0.38 (<0.001) | 0.07 (0.208) |
s-IGFBP1, r (p) | −0.24 (<0.001) | NA | −0.32 (<0.001) | 0.05 (0.370) |
HOMA-IR, r (p) | 0.38 (<0.001) | −0.32 (<0.001) | NA | 0.22 (<0.001) |
hs-CRP, r (p) | 0.07 (0.21) | 0.05 (0.370) | 0.22 (<0.001) | NA |
Poor Outcome—Time Point | n | p (3-Group) | A. Normal-Weight (BMI 18.5–25) | p A vs. B | B. Overweight (BMI 25–30) | p B vs. C | C. Obese (BMI > 30) | p A vs. C |
---|---|---|---|---|---|---|---|---|
3 months [n/total n in category, (%)] | 432 | 0.956 | 35/172 (20.3) | 0.795 | 36/187 (19.2) | 0.814 | 15/73 (20.5) | 0.972 |
2 years [n/total n in category, (%)] | 449 | 0.508 | 39/180 (21.7) | 0.287 | 33/191 (17.2) | 0.389 | 17/61 (27.9) | 0.982 |
7 years [n/total n in category, (%)] | 451 | 0.001 | 76/180 (42.2) | 0.004 | 54/193 (28.0) | 0.001 | 38/78 (48.7) | 0.337 |
Variable | N | Entire Cohort | Good Outcome (mRS 0–2) | Poor Outcome (mRS 3–6) | Good vs. Poor, p-Value |
---|---|---|---|---|---|
All patients, N (%) | 451 | 451 (100) | 283 (100) | 168 (100) | NA |
Females, N (%) | 163 | 163 (36.1) | 104 (36.7) | 59 (35.1) | 0.728 |
Males, N (%) | 288 | 288 (63.8) | 179 (63.3) | 109 (64.9) | 0.728 |
Age, years (95% CI) | 451 | 56.8 (55.8–57.7) | 55.1 (53.9–56.3) | 59.7 (58.2–61.1) | <0.001 |
BMI, kg/m2 (95% CI) | 451 | 26.6 (26.2–27.0) | 26.6 (26.1–27.0) | 26.7 (26.0–27.5) | 0.695 |
Hypertension, N (%) | 451 | 269 (59.6) | 162 (57.2) | 107 (63.7) | 0.178 |
Systolic BP, mmHg, mean (95% CI) | 442 | 146 (144–149) | 145 (142–148) | 149 (145–153) | 0.12 |
Diastolic BP, mmHg, mean (95% CI) | 441 | 84 (83–85) | 84 (82–85) | 85 (82–87) | 0.398 |
Smoking, N (%) | 451 | 169 (37.4) | 98 (34.6) | 71 (42.2) | 0.106 |
Diabetes, N (%) | 451 | 85 (18.8) | 35 (12.4) | 50 (29.8) | <0.001 |
LDL, mmol/L, mean (95% CI) | 388 | 3.33 (3.24–3.43) | 3.39 (3.27–3.52) | 3.24 (3.08–3.39) | 0.123 |
Imputed LDL, mmol/L, mean (95% CI) | 451 | 3.33 (3.25–3.42) | 3.38 (3.28–3.49) | 3.25 (3.12–3.38) | 0.138 |
hs-CRP, mg/L, mean (95% CI) | 431 | 9.58 (7.76–11.4) | 6.64 (4.60–8.68) | 14.7 (11.3–18.0) | <0.001 |
Sedentary lifestyle, N (%) | 424 | 73 (17.2) | 27 (10.3) | 46 (29.5) | <0.001 |
Previous stroke, N (%) | 451 | 88 (19.5) | 43 (15.2) | 45 (26.8) | 0.003 |
NIHSS score, mean (95% CI) | 451 | 4.92 (4.44–5.41) | 3.33 (2.89–3.77) | 7.60 (6.67–8.54) | <0.001 |
s-IGFBP1, µg/L, mean (95% CI) | 341 | 7.24 (6.36–8.13) | 5.71 (5.16–6.27) | 10.1 (7.87–12.4) | 0.015 |
Insulin, microU/L, mean (95% CI) | 430 | 15.5 (14.1–16.9) | 14.0 (12.5–15.6) | 18.0 (15.3–20.8) | 0.003 |
Glucose, nmol/L, mean (95% CI) | 427 | 6.37 (6.13–6.60) | 6.00 (5.79–6.21) | 6.99 (6.46–7.51) | <0.001 |
HOMA-IR, mean (95% CI) | 413 | 4.58 (4.09–5.07) | 3.93 (3.40–4.46) | 5.69 (4.73–6.66) | <0.001 |
(a) | ||||||
ORs for Poor Outcome (mRS 3–6) after 7 Years (BMI 18.5–30) | ||||||
Parameter | BMI 18.5–25 vs. 25–30 (ref = 1) | p | Per Log10 IGFBP-1 Increase | p | Per Log10 HOMA-IR Increase | p |
Unadjusted | 2.22 (1.33–3.72) | 0.002 | 2.96 (1.51–5.80) | 0.002 | 3.57 (1.60–7.98) | 0.002 |
Model 1 | 2.44 (1.43–4.14) | 0.001 | 2.68 (1.35–5.32) | 0.005 | 3.26 (1.44–7.39) | 0.005 |
Model 2 | 2.32 (1.30–4.14) | 0.004 | 4.67 (2.12–10.3) | <0.001 | 2.51 (1.03–6.12) | 0.043 |
Model 2 + BMI 18.5–25 | NA | NA | 4.44 (2.00–9.89) | <0.001 | 3.68 (1.42–9.49) | 0.007 |
Model 2 + IGFBP-1 | 2.21 (1.21–4.02) | 0.010 | NA | NA | 3.63 (1.41–9.35) | 0.007 |
Model 2 + HOMA-IR | 2.84 (1.54–5.22) | 0.001 | 5.67 (2.50–12.9) | <0.001 | NA | NA |
Model 2 + Diabetes | 2.97 (1.59–5.55) | 0.001 | 3.75 (1.67–8.41) | 0.001 | 1.59 (0.61–4.19) | 0.344 |
Model 2 + hs-CRP | 2.34 (1.29–4.24) | 0.005 | 4.47 (1.99–10.0) | <0.001 | 2.26 (0.91–5.65) | 0.080 |
(b) | ||||||
ORs for Poor Outcome (mRS 3–6) after 7 Years (BMI > 25) | ||||||
Parameter | BMI > 30 vs. 25–30 (ref = 1) | p | Per Log10 IGFBP–1 Increase | p | Per Log10 HOMA–IR Increase | p |
Unadjusted | 2.44 (1.29–4.62) | 0.006 | 2.01 (0.90–4.49) | 0.088 | 5.34 (1.96–14.5) | 0.001 |
Model 1 | 2.35 (1.23–4.51) | 0.01 | 1.83 (0.80–4.18) | 0.155 | 4.99 (1.81–13.8) | 0.002 |
Model 2 | 2.25 (1.08–4.71) | 0.031 | 3.81 (1.44–10.1) | 0.007 | 3.06 (0.98–9.48) | 0.053 |
Model 2 + BMI > 30 | NA | NA | 6.23 (2.12–18.32) | 0.001 | 2.39 (0.74–7.79) | 0.147 |
Model 2 + IGFBP-1 | 3.45 (1.53–7.76) | 0.003 | NA | NA | 4.54 (1.39–14.8) | 0.012 |
Model 2 + HOMA-IR | 1.97 (0.92–4.22) | 0.082 | 5.10 (1.82–14.3) | 0.002 | NA | NA |
Model 2 + Diabetes | 2.16 (1.02–4.58) | 0.045 | 3.20 (1.19–8.60) | 0.021 | 1.96 (0.57–6.70) | 0.286 |
Model 2 + hs-CRP | 2.12 (0.97–4.61) | 0.058 | 3.87 (1.39–10.7) | 0.009 | 3.95 (1.17–13.3) | 0.027 |
BMI Category | n | OR for mRS 3–6, in Low IGFBP-1 | OR for mRS 3–6, in High IGFBP-1 |
---|---|---|---|
All BMI | 330 | NA | NA |
BMI 18.5–25 | 128 | 3.00 (1.35–6.68) | 4.41 (2.00–9.73) |
BMI 25–30 | 144 | ref = 1 | 2.50 (1.14–5.51) |
BMI > 30 | 58 | 3.53 (1.35–9.26) | 4.67 (1.79–12.1) |
BMI Category | n | OR for mRS 3–6, in Low HOMA-IR | OR for mRS 3–6, in High HOMA-IR |
---|---|---|---|
All BMI | 330 | NA | NA |
BMI 18.5–25 | 128 | 3.00 (1.35–6.68) | 4.41 (2.00–9.73) |
BMI 25–30 | 144 | ref = 1 | 2.50 (1.13–5.51) |
BMI > 30 | 58 | 2.63 (0.98–7.05) | 6.15 (2.36–16.1) |
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Gadd, G.; Åberg, D.; Wall, A.; Zetterberg, H.; Blennow, K.; Jood, K.; Jern, C.; Isgaard, J.; Svensson, J.; Åberg, N.D. A Nonlinear Relation between Body Mass Index and Long-Term Poststroke Functional Outcome—The Importance of Insulin Resistance, Inflammation, and Insulin-like Growth Factor-Binding Protein-1. Int. J. Mol. Sci. 2024, 25, 4931. https://doi.org/10.3390/ijms25094931
Gadd G, Åberg D, Wall A, Zetterberg H, Blennow K, Jood K, Jern C, Isgaard J, Svensson J, Åberg ND. A Nonlinear Relation between Body Mass Index and Long-Term Poststroke Functional Outcome—The Importance of Insulin Resistance, Inflammation, and Insulin-like Growth Factor-Binding Protein-1. International Journal of Molecular Sciences. 2024; 25(9):4931. https://doi.org/10.3390/ijms25094931
Chicago/Turabian StyleGadd, Gustaf, Daniel Åberg, Alexander Wall, Henrik Zetterberg, Kaj Blennow, Katarina Jood, Christina Jern, Jörgen Isgaard, Johan Svensson, and N. David Åberg. 2024. "A Nonlinear Relation between Body Mass Index and Long-Term Poststroke Functional Outcome—The Importance of Insulin Resistance, Inflammation, and Insulin-like Growth Factor-Binding Protein-1" International Journal of Molecular Sciences 25, no. 9: 4931. https://doi.org/10.3390/ijms25094931