DOI: 10.6084/m9.figshare.21830088
title: Supplementary Material for: Associations of Inflammatory, Metabolic, Malnutrition, and Frailty Indexes with Multimorbidity Incidence and Progression, and Mortality Impact: Singapore Longitudinal Aging Study

description: <b><i>Introduction:</i></b> The detection of systemic risk factors aids in the formulation of strategies to prevent multimorbidity and its associated mortality impact. We aimed to determine the associations of inflammatory, metabolic, malnutrition, and frailty indexes with multimorbidity onset and progression and their predictions of multimorbidity-associated mortality risk. <b><i>Methods:</i></b> A prospective cohort study (Singapore Longitudinal Aging Study [SLAS]) of 5,089 community-dwelling older adults aged ≥55 years in two waves of recruitment (SLAS-1: March 2005-September 2007, SLAS-2: January 2013-August 2018). Baseline variables included inflammatory (neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR)) and metabolic profiles (atherogenic index of plasma (AIP), triglyceride-glucose index of insulin resistance (TyG)), physical frailty, and nutritional risk (Mini Nutritional Assessment-Short Form (MNA-SF), Nutritional Screening Initiative (NSI), Elderly Nutritional Indicators for Geriatric Malnutrition Assessment (ENIGMA)). At follow-up, 3–5 years after the baseline interview, incident multimorbidity (≥2 chronic diseases) was determined among multimorbidity-free participants (<i>N</i> = 1,657) and worsening multimorbidity (increase of ≥2 chronic diseases) among participants with baseline multimorbidity (<i>N</i> = 1,207). Mortality in all participants and those with multimorbidity (<i>N</i> = 2,291) was determined up to 31 December, 2016. Odds ratio (OR), hazard ratio (HR), and 95% confidence intervals (95% CI) were estimated in multivariate logistic and Cox regression models, in base model adjustments for age, sex, ethnicity, housing type, smoking, and a number of comorbidities, and further stepwise selection adjustment for other systemic risk indexes. <b><i>Results:</i></b> At baseline, NLR, LMR, AIP, TyG, physical frailty, ENIGMA, NSI, and MNA-SF were significantly associated with prevalent multimorbidity (<i>p</i> &lt; 0.001). Among multimorbidity-free participants, LMR, TyG, and ENIGMA were significantly associated with incident multimorbidity in both the base model and further stepwise selection models: LMR (OR = 0.87, 95% CI: 0.81–0.94), TyG (OR = 1.36, 95% CI: 1.06–1.75), and ENIGMA (OR = 1.15, 95% CI: 1.02–1.30). Among participants with baseline multimorbidity, NLR, LMR, and TyG significantly predicted worsened multimorbidity at follow-up in base model analysis, and LMR (OR = 0.72, 95% CI: 0.60–0.86) and TyG (OR = 1.96, 95% CI: 1.24–3.09) remained as independent predictors in further stepwise selection models. Among participants with prevalent multimorbidity, NLR, TyG, frailty, MNA, and ENIGMA were significantly associated with mortality risk with base model adjustments and further stepwise selection models: NLR (HR = 1.20, 95% CI: 1.10–1.32), TyG (HR = 1.27, 95% CI: 1.04–1.54), frailty (HR = 1.22, 95% CI: 1.10–1.36), ENIGMA (HR = 1.13, 95% CI: 1.05–1.22), MNA (HR = 0.91, 95% CI: 0.85–0.97). A combined systemic risk index shows increasing quartiles, adjusted for age, sex, housing, and smoking status, significantly predicting mortality risk. <b><i>Discussion/Conclusion:</i></b> The onset and progression of multimorbidity and its mortality impact are driven by systemic factors, including inflammation, metabolic dysfunction (insulin resistance), malnutrition, and frailty. The measurement of these systemic factors using simple, inexpensive clinical and blood chemistry tools can help in strategies to prevent and reduce its mortality impact.