Integrative lipidomics and metabolomics for system-level understanding of the metabolic syndrome in long-term treated HIV-infected individuals

22 People living with HIV (PLWH) require life-long anti-retroviral treatment and often present 23 with comorbidities such as metabolic syndrome (MetS). A systematic lipidomic 24 characterization and its association with metabolism is currently missing. In this study, we 25 included 100 PLWH with MetS and 100 without MetS from the Copenhagen comorbidity in 26 HIV infection (COCOMO) cohort to examine whether and how lipidome profiles associated 27 with MetS in PLWH. We combined several standard biostatistical, machine learning, and 28 network analysis techniques to investigate the lipidome systematically and 29 comprehensively. Our observations indicate an increased abundance of the glycerolipids 30 and an association between structural composition patterns of glycerolipids in PLWH with 31 MetS. Further integration of the key metabolites identified earlier in the same population 32 and clinical data with lipidomics suggest disruption of the glutamate and fatty acid 33 metabolism. suggest their involvement in pathogenesis of PLWH with MetS. 34

35 HIV-1, metabolic syndrome, antiretroviral treatment, machine learning, systems biology 36 37 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 2

38 Combination antiretroviral therapy (cART) was introduced in 1995 and has increased life 39 expectancy for people living with HIV (PLWH) over the last decades. However, an 40 increase in incidences of comorbidities such as obesity, type 2 diabetes (T2D), and 41 cardiovascular disease (CVD) related to metabolic syndrome (MetS) (i.e., abdominal 42 obesity, hypertension, elevated levels of triglycerides, dyslipidemia, and altered glucose 43 levels), has become a growing concern in successfully treated PLWH. In chronic HIV 44 infection, complicated interactions between effects of persistent low-grade immune 45 activation, metabolic toxicity from cART, and non-HIV related risk factors may increase the 46 risk of MetS in PLWH. However, the pathophysiology of MetS in PLWH is still incompletely 47 understood (Babu et al., 2019; Chai et al., 2019; Gelpi et al., 2020). 48 cART is known to be associated with changes in fat distribution (lipodystrophy and 49 dyslipidemia) and metabolic abnormalities (Freitas et al., 2011). Changes in fat distribution 50 is a common and known side effect of cART (i.e., affecting up to half of HIV-infected 51 patients receiving cART) and is not limited to a specific drug or active agent. Several 52 studies have investigated the association of HIV infection and the association of cART with 53 metabolic abnormalities related to MetS (e.g., abdominal obesity, T2D, and CVD) (Freitas 54 et al., 2011; Gelpi et al., 2018). These studies have focused on conventional blood lipids, 55 such as triglyceride level and total cholesterol. These biomarkers may not sufficiently 56 reflect the complex alterations of the lipid metabolism in PLWH with MetS. Thus, studies 57 exploring the complexity of the alterations of the lipid metabolism are needed to explore 58 the underlying biomolecular mechanisms of this phenotype. 59 Untargeted lipidomics is an approach that assesses hundreds of lipid species across 60 multiple biological pathways, which are present in biological samples (i.e., the lipidome). 61 Lipidomics may help in the discovery of new patterns and disease markers associated with 62 MetS in PLWH (Chai et al., 2019). Plasma lipidomics studies in the general population 63 have identified several lipid species within the lipidome to be associated with features of 64 MetS (Meikle and Christopher, 2011). In addition, obesity has been shown to increase the 65 content of almost all detectable diacylglyceride (DAG) and triacylglyceride (TAG) lipid 66 species, along with several cholesterol fatty acids (CE), phosphatidylcholine (PC), 67 phosphatidylethanolamine (PE), and lysophosphatidylcholine (LPC) in a general 68 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 3 population (Graessler et al., 2009). The pathophysiology and alterations of the lipidome 69 have yet to be explored. In a prior work from our group, we identified key metabolites, 70 which influenced and altered the metabolome of PLWH with MetS (Gelpi et al., 2021). 71 An exploratory analysis of the lipidome comparing PLWH without MetS and PLWH with 72 MetS was conducted to identify a set of key lipids that define the mechanism of the lipid 73 abnormalities of MetS in the context of HIV infection. Also, we have performed advanced 74 network analysis that contributed to reveal deeper underlying patterns within the 75 metabolome (i.e., the polar metabolome and lipidome) of PLWH with MetS. Additionally, 76 we investigated the influence of clinical demographic parameters on integrative 77 metabolomics and lipidomics to provide snapshots of the biological phenotypes linked with 78 MetS in PLWH. Our study is the first to provide a comprehensive lipidomics 79 characterization in PLWH with MetS and integrate the lipidomics with a set of key 80 metabolites. The findings can further our understanding of lipid disruption in PLWH with 81 MetS, which may inform future clinical intervention strategies. 82 83

84 Machine learning highlights differences in key lipids in PLWH with MetS 85 PLWH with MetS (n=100) and PLWH without MetS (n=100) were included from the 86 COCOMO study (Table 1). VAT and SAT significantly differed between the two groups (p-87 value<0.001). The variables immunodeficiency, exposure to early-generation ART, and 88 ART drugs based on their mode of action (i.e., NRTI, NNRTI, PI, INSTI, other/unknown), 89 did not significantly differ (p-value>0.05). 90 We further applied a number of univariate and machine learning approaches to 91 characterize the effect of MetS in HIV-infected following long-term cART treatment, and to 92 investigate the underlying biological mechanisms of MetS (Figure 1). The lipidomic dataset 93 consisted of 917 unique lipid species including 602 glycerolipids, 228 94 glycerophospholipids, 61 sphingolipids, and 26 steroids. We observed 618 and 584 95 significantly differentially abundant lipids between PLWH without MetS and PLWH with 96 MetS (Sup. Data File S1, FDR < 0.001), using Mann-Whitney U and limma, respectively. 97 Moreover, PLS-DA was used to identify variations between the groups based on lipid 98 concentrations, by exploiting its ability to handle a greater number of features compared to 99 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 4 samples. Separation of the two groups were indicated by a score plot, where the two first 100 orthogonal components explained half of the variance in the data with 45% and 5%, 101 respectively. We found 516 lipids with VIP values >1, Q2Y = 0.319 (Sup. Data File S1). To 102 obtain a better model performance we could increase the sample size. Finally, we created 103 three types of RF models, minimal-optimal (‘Min’), geometric mean (‘Mid’) and all-relevant 104 (‘Max’) models, which represented feature selection with minimal number of 105 misclassifications, where we observed a good performance of all models (Figure 2a, AUC 106 > 82.9%). Then, we identified 13 lipids as strongest predictors of separating PLWH without 107 MetS and PLWH with MetS (Figure 2b, ’Max’ MUVR model, AUC > 83%), where the 108 glycerolipid classes, DAGs and TAGs were found to have the greatest significance in 109 group separation. 110 The number of significant lipids identified by each of the four methods varied greatly (Sup. 111 Data File S1). However, we observed 13 differentially abundant lipids between PLWH 112 without MetS and PLWH with MetS (Figure 2c) which were consistently identified in all four 113

(i.e., Mann-Whitney U, limma, PLS-DA, and RF). These 13 key lipids and 11 key 114 metabolites (Table 2) indicated relatively good separation between PLWH without MetS 115 and PLWH with MetS on sample clustering (Figure 2d). Furthermore, we observed higher 116 abundance level of the key lipids between the groups (Figure 2e). 117 Structural interpretation of lipids indicates compositional lipid patterns 118 We then examined the structural characteristics of the lipidome by lipid class in terms of 119 FA carbon number and saturation level (Figure 3). We observed an increase of ceramide 120 (CER), DAG, dihydroceramide (DCER), lysophosphatidylethanolamine (LPE), 121 monoacylglyceride (MAG), PE and TAG, in PLWH with MetS compared to PLWH without 122 MetS, and a decrease in hexosylceramide (HCER) and lactosylceramide (LCER) (Figure 123 3, FDR 0.7). An increased significantly differential abundance of 124 DAGs and TAGs was observed, indicated by the symbol and red color. The TAGs tended 125 to display a higher abundance of polyunsaturated lipids (i.e., a double-bond content 126 between 2-5) with long-chain fatty acids (LCFA) (i.e., C4856) (Figure 3, FDR 0.7). Additionally, TAGs displayed the largest amount of lipid species. DAGs 128 showed a tendency of increase in both saturated and unsaturated lipids (i.e., a double-129 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 5 bond content between 0-6) with LCFA (i.e., C30-40) (Figure 3, FDR0.7). 131 Clinical and omics integrated network identifies biomolecular patterns 132 Seeking to test whether and how any coordinated patterns of association were present 133 throughout the samples, we generated weighted lipid-metabolite networks. As we aimed to 134 understand the relationship between lipidomic profiles with those metabolites associated 135 with MetS in PLWH. While retaining only informative metabolites we examined the 136 relationship between key metabolites previously identified [11] with the entire lipidome. 137 Briefly, we associated clinical variables with the identified communities and within the most 138 central community we identified associations between clinical variables and each 139 biomolecule. 140 The fully connected biological network comprised 18430 edges and 917 nodes and 141 displayed markedly distinct behavior from the null network (Sup. Table S1 and Sup. Figure 142 S1). A community analysis on the biological network identified three communities of 143 strongly interconnected lipids and metabolites (Sup. Table S2). Centrality properties were 144 evaluated identifying c1 as the most central community in the network (Figure 4a), which 145 captured most coordinated differential abundance changes. Community c1 had the largest 146 community size (size = 339) and largest community average degree (avg. degree = 147 534.63) (Sup. Table S2). 148 Structural and functional characterization of these communities [25] (Sup. Figure S2) 149 indicated that glycerolipids and especially TAGs were enriched in both c1 and c2 (Figure 150 4a, FDR < 0.05). Interestingly, a coordinated structural composition pattern of the 151 glycerolipids displayed average lower carbon number and average lower double-bond 152 content in c1, compared to c2 (Sup. Table S3). Community c3 was not further addressed, 153 as the two other communities were interpreted to be of more importance due to their node-154 size and average degree (Sup. Table S2). We identified a positive association between 155 community c1 with the clinical variables MetS, VAT and exposure to early-generation ART 156 (Sup. Table S4, FDR < 0.12, illustrated in Figure 4a). In turn, community c2 was positively 157 associated with MetS, however with a lower estimate compared to c1 (Sup. Table S4, FDR 158 < 0.12). Log-fold changes indicated up-regulation of lipids in PLWH with MetS compared 159 to PLWH in both community c1 and c2 (Figure 4b, limma, FDR < 0.001). 160 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 6 Furthermore, we observed a positive association between lipids (DAGs and TAGs) and 161 VAT (Sup. Table S5, FDR < 0.01). TAGs tended to consist of polyunsaturated lipids (i.e., a 162 double-bond content ≥ 2) with LCFA (i.e., C48-54). Interestingly, we also observed that 163 TAGs with LCFA (i.e., C42-48) and a low double-bond content (i.e., ≤ 2) were positively 164 associated with use of NNRTI (Sup. Table S5, FDR < 0.07). Additionally, four out of the 13 165 key lipids (i.e., TAG(52:2)-FA(16:0), TAG(52:2)-FA(18:1), DAG(16:0/18:1), and 166 TAG(54:3)FA(20:3)) were all found to be independently associated with VAT (Sup. Table 167 S5, FDR < 0.01). Finally, one out of the 11 key metabolites (i.e., glutamate) was also 168 found to be independently associated with VAT (Sup. Table S5, FDR < 0.01). 169 The top 10% most interconnected biomolecules, found according to their degree, were all 170 glycerolipids within the classes DAG and TAG (Sup. Table S6). Three out of the 13 key 171 lipids (i.e., TAG(52:2)-FA(16:0), TAG(52:2)FA(18:1) and TAG(54:3)-FA(20:3)) were ranked 172 among the top 10% most interconnected biomolecules in c1. Thus, these three lipids were 173 interpreted to be among those biomolecules influencing the behavior of the global network 174 the most. It should be noticed that the structural composition of all three lipids were 175 polyunsaturated TAGs with LCFA and were all found to be positively associated with VAT 176 (Sup. Table S5, FDR < 0.01). No further information was found on the three glycerolipids 177 from the human metabolome data base (HMDB) or KEGG. 178 The local network of community c1 included six out of the 13 key lipids (i.e., TAG(54:3)-179 FA(20:2), DAG(16:0/18:1), TAG(52:2)-FA16:0), TAG(54:3)-FA20:3), TAG(52:2-FA(18:1) and 180 TAG(44:0)-FA(18:0)) (Figure 4c). All of the six key lipids within c1 were glycerolipids; five 181 TAGs and one DAG. Interestingly, we observed that all five TAGs were polyunsaturated 182 with a double-bond content between 2 and 4 with a FA carbon number between C52 and 183 C54. Moreover, the key lipids were found to be interconnected with 6 of the 11 key 184 metabolites within c1. Finally, all 13 key lipids and 7 metabolites within the global network 185 were found to be interconnected with each other. 186 187

188 The integrative plasma lipidomics and metabolomics analysis in a large HIV-cohort of 189 PLWH with and without MetS resulted in three main findings that suggests a system-level 190 understanding of MetS in PLWH. First, our data suggested an increased abundance of the 191 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 7 glycerolipids DAGs and TAGs in PLWH with MetS. Second, the comprehensive network 192 integration of the lipidomics (analyzed in this study) and metabolomics (previously 193 analyzed (Gelpi et al., 2021)) data suggested interactions between specific glycerolipids 194 structural composition patterns and key metabolites involved in the glutamate metabolism. 195 Finally, our data also indicated a relationship between the structural composition patterns 196 of these specific glycerolipids structural composition patterns with HIV and MetS-specific 197 clinical variables, suggesting their involvement in driving the disease pathogenesis in 198 PLWH with MetS. 199 In our study, we found 13 key glycerolipids from the classes DAG (n = 2) and TAG (n = 11) 200 to be significantly altered between PLWH with and without MetS. It is worth noting the 201 structural composition of the 13 lipids. The two DAGs [DAG(16:0/18:1) and 202 DAG(16:0/18:3)] consist of unsaturated LCFA (i.e., C34 and 1-3 double-bonds). 203 Additionally, 10 out of the 11 TAGs were polyunsaturated LCFA (i.e., C52-54 and a 204 double-bond content of 2-5). The last TAG had a lower carbon number of C44, compared 205 to the others and was saturated. These findings support previous findings of MetS in 206 general populations that showed that lipids (especially TAGs) with lower carbon number 207 (i.e., C44-54) and lower double-bond content (i.e., 1-4) were associated with an increased 208 risk of T2D. Moreover, it had been observed that an increase in DAGs was associated with 209 hypertension, another MetS-related factor (Hinterwirth et al., 2014). The structure of the 210 FAs is a useful indication of the functionality of the lipid metabolism. Increased 211 accumulation of LCFA such as C(16:0), C(16:1), C(18:0), and C(18:1) suggests increased 212 biosynthesis under MetS-conditions. Such chain compositions are observed among our 13 213 identified key lipids both in the DAGs and TAGs. Additionally, to the observed pattern of 214 LCFAs, another study suggests LCFAs might cause impairment of mitochondria functions 215 (Hafizi Abu Bakar et al., 2015). 216 Integrative metabolomics and lipidomics can unravel the complex relationships between 217 metabolites and lipid classes, thus provide a comprehensive view of the metabolic state 218 related to a disease phenotype. We employed network analysis by integrating the key 219 metabolites previously identified as biomarkers in PLWH with MetS (Gelpi et al., 2021) and 220 the lipids with the clinical features (phenomics). Interestingly, we observed that community 221 c1 contained glycerolipids with a lower carbon number and lower double-bond content 222 compared to c2. Community c1 was further investigated and we found that c1 positively 223 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 8 associated with the clinical variables MetS, VAT, and exposure to early-generation ART. 224 Our findings are related to previous findings that showed TAGs with a lower carbon 225 number and lower double-bond content play a considerable role in MetS (Rhee et al., 226 2011; Stegemann et al., 2014). Additionally, our results suggest that exposure to early-227 generation ART (i.e., thymidine analogues/Didanosine/Indinavir) and increased VAT may 228 also lead to a lower carbon number and double-bond content in glycerolipids, suggesting a 229 role for polyunsaturated glycerolipids with LCFA (i.e., especially TAG(52:2)-FA(16:0), 230 TAG(52:2)-FA(18:1) and TAG(54:3)-FA(20:3)) in the metabolic patterns in PLWH with 231 MetS. 232 Previous studies have investigated the association of lipidomics profiles (211 lipids) with 233 the progression of CVD in PLWH receiving ART treatment with carotid artery 234 atherosclerosis, compared to HIV-negative individuals (Chai et al., 2019). The study 235 showed elevation in lipid species with polyunsaturated LCFAs (i.e., C13-21 and double-236 bond content ≥ 2) in patients with atherosclerosis, which is also observed in PLWH with 237 MetS in our study. Additionally, their study suggested significant alterations in lipid species 238 such as cholesteryl ester (CE), LPC, lysophosphatidylethanolamine (LPE), PC, PIs and 239 ceramide (CER). Other studies in both HIV and HIV-negative populations also found 240 alterations in levels of other lipid species (different from DAG and TAG), to be associated 241 with MetS-factors. This includes CE, CER, LPC, PC, PE and sphingomyelin (SM) (Gelpi et 242 al., 2018; Stegemann et al., 2014). Some of these lipid species were also altered in our 243 study population (i.e., CE, CER, PE and SM), however the glycerolipids showed strongest 244 predictive values. Our findings of coordinated abundance shifts in glycerolipids may be 245 due to our considerably larger amount of quantified lipid species (n = 917) compared to 246 other studies (Chai et al., 2019; Rhee et al., 2011; Stegemann et al., 2014) quantified < 247 215 lipid species each). In the same cluster (c1) we observed two trends with respect to 248 coordinated abundance shifts in glycerolipids. First, TAG species with carbon numbers 249 between C48-54 and double-bond content ≥ 2, together with DAG species with carbon 250 number between C32-36 and double-bond content ≥ 1 associated positively with VAT 251 (FDR < 0.01). This finding correlates with previous studies of MetS factors in HIV-negative 252 cohorts (Rhee et al., 2011; Stegemann et al., 2014). Second, TAG species with carbon 253 numbers between C42-48 and double-bond content ≤ 2 positively associated with the use 254 of ART drugs containing NNRTIs (FDR<0.07). The latter trend supports previous findings 255 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 9 suggesting that the NNRTIs drug efavirenz introduce dysfunction in the mitochondria by 256 inducing increased levels of lipids (Blas-García et al., 2010). To our knowledge, we 257 present here the first evidence of association with specific structural composition lipid 258 profiles. Both exposure to early-generation ART and the use of NNRTIs drugs have shown 259 to cause disruption of the mitochondrial functions in previous studies (Blas-García et al., 260 2010; Trevillyan et al., 2018). 261 The composition-specific glycerolipids correlated with some of the previously identified key 262 metabolites linked to the perturbations of the glutamate metabolism in PLWH with MetS 263 (Gelpi et al., 2021). This finding correlates with previous studies of MetS in HIV-negative 264 populations, which found branched-chain amino acids (BCAAs) (i.e., leucine, isoleucine 265 and valine) as one of the major metabolite groups dysregulated in obese individuals 266 together with increased concentrations of glutamate, which is the first step of the BCAAs 267 catabolism (Rangel-Huerta et al., 2019; Wang et al., 2020). Additionally, the polar 268 metabolite acylcarnitine (abbreviated PC/3-MAPC, Table 2), an important member of the 269 fatty acid metabolism, was found to be significantly down-regulated in PLWH with MetS. 270 This molecule facilitates the transportation of LCFAs into the mitochondria for catabolism 271 through β -oxidation (Kerner and Hoppel, 2000). Besides glutamate and 4-272 hydroxyglutamate being a part of the glutamate metabolism, other identified key 273 metabolites were found to be a part of mitochondrial processes, which have important 274 energetic functions (e.g., regulate insulin secretion). These metabolites belonged to the 275 isoleucine metabolism (i.e., 1-carboxyethylleucine, isoleucine) and the TCA cycle (i.e., γ -276 glutamylglutamate, α -ketoglutarate) (Gelpi et al., 2021). 277 To our knowledge, correlation between composition specific lipids and polar metabolites 278 has not been seen in previous studies of MetS. In our study the polar metabolites and key 279 lipids are clustered together in the same community (c1) and are positively correlated with 280 each other. These findings suggest a pattern and relationship between polar metabolites 281 involved in the glutamate metabolism and glycerolipids having a specific structural 282 composition of their FA chains (i.e., polyunsaturated LCFAs). To identify this cluster of 283 associated lipids and polar metabolites as potential biomarkers in PLWH with MetS, our 284 findings should be validated in other cohorts. 285 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 10 Strengths and limitations 286 Main strengths of this study include a large well-characterized group of PLWH with or 287 without MetS matched on MetS, sex, and age. Furthermore, the use of a fully quantitative 288 lipidomics (i.e., >910 quantified lipid species) methodology allowed us to conduct a 289 thorough analysis of the systematic lipid profiling and its association with metabolites and 290 clinical factors, by using a combination of standard biostatistical, machine learning, and 291 network analysis techniques. The present study also has limitations, such as the cross-292 sectional design, as no conclusions on causality could be drawn, we were only able to 293 assess the prevalence of the diseases in the plasma samples. Finally, despite the largest 294 study population conducted to date to type comprehensive lipid profile in PLWH, the 295 relatively small sample size of the cohort is also considered as a limitation to this study. 296

297 In conclusion, our study suggests alterations in both the fatty acid metabolism and 298 glutamate metabolism, which both are depending on well-functioning mitochondria. A 299 synergistic effect of different factors (i.e., an increased proinflammatory state induced by 300 HIV, age-related pathophysiological changes, exposure to early-generation ART, and the 301 use of ART with the active agent NNRTIs), which perturb the functions within the biological 302 system of HIV-infected, could play a part in the alterations of the identified biological 303 mechanisms in the phenotype PLWH with MetS. Moreover, our findings also suggest the 304 importance of the structural composition patterns of glycerolipids in the context of excess 305 risk of MetS-related comorbidities among PLWH, such as T2D and CVD. A better 306 knowledge of the structural composition of various glycerolipid species and their 307 association with polar metabolites and clinical variables expands our understanding of the 308 role of lipids in PLWH with MetS. Once we have a better understanding of structural 309 composition patterns of lipid species and their role in biological pathways, novel lipid 310 biomarkers and therapeutic targets could be established to avoid metabolic abnormalities 311 and accelerated aging in PLWH with MetS, as an extension to conventional blood lipid 312 measurements. 313 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 11

314 The computations were enabled by resources in project [Dnr. SENS2017550] provided by 315 the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by 316 the Swedish Research Council through grant agreement no. 2018-05973. The study is 317 funded by Rigshospitalet Research Council, Danish National Research Foundation 318 (DNRF126) NovoNordisk Foundation. UN acknowledge the support received from 319 Swedish Research Council Grants (2017-01330 and 2018-06156). 320 321 Author contributions: 322 Conceptualization and clinical study designing: S.D.N, S.O.V, U.N, M.G, A.D.K, D.M; 323 Clinical data and biobank: S.D.N., M.G., A.D.K., J.H., M.T.T., and H.U. Methodology: 324 S.O.V., R.B., and U.N., Formal analysis: S.O.V. and R.B., Clinical interpretation: S.O.V, 325 U.N., R.B., S.D.N., M.G., A.D.K. and D.M., Supervision: R.B., A.D.K, M.G., U.N., and 326 S.D.N., Resources: U.N. and S.D.N., Writing (original draft): S.O.V., Writing (review and 327 editing): R.B., A.D.K, M.G., H.U, M.T.T, J.H, D.M, U.N., and S.D.N., Visualization: S.O.V., 328 R.B., and U.N., Project administration: U.N. and S.D.N, Funding acquisition: U.N. and 329 S.D.N. All authors discussed the results, commented, and approved the final version of the 330 manuscript. 331 Declaration of Interests: The authors declare no competing interests. 332 Figures and tables legends 333 Table 1: Clinical and demographic characteristics compared between PLWH without 334 MetS and PLWH with MetS. P-values in bold indicates a significant difference in the 335 concerned variables between the two groups. Immunodeficiency was defined as lowest 336 CD4+ T-cell count <200 cells/ µl or previous AIDS condition and exposure to early-337 generation ART was defined as patients medicated with thymidine analogues, Didanosine 338 and/or Indinavir. 339 Table 2: Identified key lipids and key metabolites. Overview of key lipids and key 340 metabolites with significant differential abundance between PLWH without MetS and 341 PLWH with MetS. Listed in alphabetical order. 342 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 12 Figure 1: Overview of study workflow. Analysis pipeline for characterizing the effect of 343 MetS in HIV-infected following ART treatment and investigating the underlying biological 344 mechanisms of PLWH with MetS (created with BioRender.com). 345 Figure 2: Lipidomics analyses of PLWH without MetS vs PLWH with MetS identifying 346 key lipids differentiating the two groups. (a) Performance of random forest (RF) 347 models. Receiver operating characteristic (ROC) curve with area under the curve (AUC) 348 values for the three MUVR models. (b) Important prediction variables separating PLWH 349 without MetS from PLWH with MetS based on lipidomics, diacylglycerol (DAG) and 350 triacylglycerol (TAG). Variables importance on projection (VIP) score plot for the ’Max’ 351 MUVR model, where lower rank indicates better group separation, thus better prediction 352 variables in the model classification. (c) Intersection of methods identifying key lipids. 353 UpSet plot showing number of significant lipids found via four statistical methods (RF, 354 PLS-DA, limma and Mann-Whitney U test). Note the 13 lipids (intersection size on the y-355 axis) simultaneously identified by all four methods. (d) Separation of PLWH without MetS 356 from PLWH with MetS based on identified key biomolecules. Principal component analysis 357 (PCA) on key biomolecules, where lipidomics and metabolomics data were separated by 358 the 13 identified key lipids and 11 identified key metabolites (Table 2). Ellipses show the 359 95% confidence interval of the data. (e) Boxplot of lipid concentration of the identified key 360 lipids, which consist of DAGs and TAGs. 361 Figure 3: Structural differences of lipidomic profile of PLWH without MetS vs PLWH 362 with MetS. Heatmaps for each lipid class showing the structural lipid composition 363 differences between PLWH without MetS and PLWH with MetS. Each lipid specie is 364 shown as a rectangle and the color shows the abundance difference (red: higher in PLWH 365 with MetS; white: no difference; blue: lower in PLWH with MetS), the lipids were organized 366 by the lipid size (y-axis) and level of saturation (x-axis). Lipids with statistically significant 367 difference between the two groups were highlighted with a symbol. P-values have been 368 FDR adjusted. 369 Figure 4: Global and local biomolecular network of PLWH without MetS vs PLWH 370 with MetS. (a) Global network illustrating the associated clinical variables and ontology 371 terms with each community. Network of positive correlations between lipids and 372 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 13 metabolites (FDR 0.38), colored based on the three identified 373 communities, c1 (blue), c2 (green) and c3 (red). Communities are connected with 374 associated clinical variables (FDR < 0.12) and ontology terms (FDR < 0.05). Black circled 375 lipids and metabolites corresponds to identified key lipids and key metabolites (Table 2). 376 (b) Global network illustrating up and down regulated lipids in PLWH with HIV. (c) Local 377 network of community c1 highlighting key biomolecules. Biomolecular correlations within 378 community c1 (FDR 0.38). Black circled and named 379 biomolecules corresponds to the identified key lipids and metabolites within c1. 380 381 382

383 Study designing, patients: 384 We obtained data from the Copenhagen comorbidity in HIV infection (COCOMO) study 385 [12], an ongoing non-interventional, observational, longitudinal cohort study with the aim of 386 assessing the burden of non-AIDS comorbidities in PLWH. Sample collections and 387 quantifications of the COCOMO cohort have previously been described (Gelpi et al., 2018; 388 Ronit et al., 2016). Of the 1099 participants in the COCOMO study, 100 PLWH ≥ 40 years 389 old were included and matched according to age, sex, duration of cART, smoking status 390 and current CD4+ T-cells count to 100 PLWH without MetS (Gelpi et al., 2018; Ronit et al., 391 2016). MetS was defined as ≥ 3 of the following: (1) waist circumference ≥ 94 cm in men 392 and ≥ 80 cm in women, (2) systolic blood pressure ≥ 130 mm Hg and/or diastolic blood 393 pressure ≥ 85 mm Hg and/or antihypertensive treatment, (3) non-fasting plasma 394 triglyceride level ≥ 1.693 mmol/L, (4) HDL level ≤ 1.036 mmol/L in men or ≤ 0.295 mmol/L 395 in women, and (5) self-reported diabetes and/or antidiabetic treatment and/or plasma 396 glucose level ≥ 11.1 mmol/L (Alberti et al., 2006). For each individual we collected clinical 397 data from the COCOMO database with the following 13 HIV- and MetS specific variables. 398 MetS, sex, age, ethnicity, immunodeficiency (i.e., lowest CD4+ T-cell count <200 cells/ µl 399 or previous AIDS condition), exposure to early-generation antiretroviral therapy (ART) (i.e., 400 medicated with thymidine analogues, Didanosine and/or Indinavir), visceral adipose tissue 401 (VAT) [cm2], subcutaneous adipose tissue (SAT) [cm2] and ART drugs including the 402 active agents; nucleotide reverse transcriptase inhibitors (NRTIs), non-nucleotide reverse 403 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 14 transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), integrase strand transfer 404 inhibitors (INSTIs), and other/unknown active agents). Furthermore, a lipidomics dataset 405 (see below) and a metabolomics dataset with 11 key metabolites (i.e., 1-406 carboxyethylisoleucine, 4cholesten-3-one, 4-hydroxyglutamate, α -ketoglutarate, carotene 407 diol (2), γ -glutamylglutamate, glutamate, glycerate, isoleucine, pimeloylcarnitine/3-408 methyladipoylcarnitine (C7-DC) (PC/3-MAPC), palmitoyl-409 sphingosinephosphoethanolamine (d18:1/16:0) (PSP)) previously identified by using a 410 combination of standard biostatistical, machine learning and network analysis techniques 411 (Gelpi et al., 2021), were collected. Ethical approval was obtained by the Regional Ethics 412 Committee of Copenhagen (COCOMO: H-15017350). Written informed consent was 413 obtained from all participants. 414 Plasma lipidomic profiling 415 Untargeted lipidomic profiling was performed on plasma samples collected at baseline in 416 COCOMO through the Complex Lipid Panel TM technique (Metabolon Inc, Morrisville, NC 417 27560, USA). Briefly, lipids were extracted from the bio-fluid using automated BUME 418 extraction (Löfgren et al., 2012). Lipids were then transferred to vials for infusion-MS 419 analysis. Samples were analyzed via positive and negative mode electrospray. Lipid 420 species were quantified by taking the ratio of the signal intensity of each target compound 421 to that of its assigned internal standard, then multiplying by the concentration of internal 422 standard added to the sample. Lipid class concentrations were calculated, and fatty acid 423 (FA) compositions were determined by calculating the proportion of each class comprised 424 by summation of individual FAs. All the lipid quantifications were median-centered and 425 missing values were minimum-imputed per lipid species. We further removed variables 426 with zero or near-zero variance from the dataset using nearZeroVar (i.e., 5%, n = 46 of 427 963). 428 Statistics and bioinformatics analysis 429 All the analyses were carried out in R 4.0.3 (Team, 2016). Clinical characteristics between 430 PLWH without MetS and PLWH with MetS were compared using the Mann–Whitney U test 431 (continuous variables) and chi-square test (categorical variables). Dimension reduction of 432 the key lipids and previously identified key metabolites from the same cohort (Gelpi et al., 433 2021) were carried out using principal component analysis (PCA). Structural interpretation 434 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 15 of the lipidome was carried out through lipidomeR (Suvitaival and Legido-Quigley, 2020). 435 We applied different complimentary methods to compare the groups. The normality of the 436 lipidomics data were tested through Kolmogorov-Smirnov test and density plots (Checa et 437 al., 2015). The Mann-Whitney U test was applied to raw data and a subset of lipids with an 438 FDR<0.001, was derived. Log-transformed data were tested for differential abundance 439 using limma and significant lipids with an false discovery rate (FDR)<0.001, were derived 440 (Ritchie et al., 2015). Binary classification modelling was carried out by partial least 441 squares discriminant analysis (PLS-DA) using ropls (Thévenot et al., 2015), where a 442 subset of variables with variables importance on projection (VIP) score >1 was derived. 443 Random forest (RF) was carried out using MUVR [https://github.com/CarlBrunius/MUVR], 444 which is developed to create results that are robust with a small sample set. Variables 445 from the optimal RF modelling performance were selected according to rank. Model 446 performance was evaluated by using the Q2Y and area under the receiver operating 447 characteristic (AUROC) for PLS-DA and RF, respectively. 448 Pathway enrichment was tested from the limma output (FDR < 0.1) with Ingenuity Pathway 449 Analysis (IPA) (Qiagen, US) and MetaboAnalyst (Chong et al., 2019) (limma, FDR< 0.1). 450 The FDR were controlled for by using the Benjamin-Hochberg (BH) method (Checa et al., 451 2015). 452 Network analysis 453 Network analyses were used to build a biological network consisting of lipids (n = 917) and 454 previously identified key metabolites (n = 11) (Gelpi et al., 2021) after Spearman’s rank 455 correlation across all species. Edges connecting nodes (i.e., biomolecules) were weighted 456 based on positive correlations. This network was compared against a null model attained 457 from a random network with the same number of nodes and edges based on the Erdos-458 Renyi model (Barabási and Oltvai, 2004). All networks were built through the Python 459 module igraph.(Csardi and Nepusz, 2006) Communities within the biological network were 460 detected through the Leiden algorithm (Traag et al., 2019). Communities were 461 characterized functionally and phenotypically through the lipid specific ontology web-tool, 462 LION/web (Molenaar et al., 2019). LION/web was used to determine lipid ontology trends 463 within each community, using all lipids from the network as background list. Separate 464 analyses on each network community with all lipids as background list was uploaded to 465 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 16 LIPEA to identify lipid pathway enrichment (Acevedo et al., 2018). Community association 466 with clinical parameters was determined through logistic and linear regression in R. 467 Network visualization was performed using Cytoscape 3.5.1 (Shannon et al., 2003). 468 Data and Code Availability: 469 The lipidomics data datacan be obtained from the dx.doi.org/10.6084/m9.figshare.14509452 470 and metabolomics dx.doi.org/10.6084/m9.figshare.14356754 471 All the codes are available at github: https://github.com/neogilab/COCOMO_lipidomics 472 Supplementary Files: 473 Supplementary Data File S1: Overview of statistical outcomes from Mann Whitney U 474 test, limma, PLS-DA and random forest. Outcome from statistical and machine learning 475

identifying lipid species differentiating PLWH without MetS from PLWH with 476 MetS. The table includes significant lipid species and their super pathway together with 477

from the four used methods Mann Whitney u test (pvalue and FDR adjusted 478 pvalues), limma (pvalue and FDR adjusted pvalues), PLS-DA (variable importance on 479 projection (VIP) values) and random forest (lowest rank indicating strongest predictors of 480 separating PLWH without MetS from PLWH with MetS). Cells with hyphens illustrates that 481 the concerned lipid specie is found not to significantly differentiating the two groups by the 482 concerned method. 483 Supplementary Table S1: Network properties of the positive and random network. 484 Including type of network (Network), node count (Nodes), edge count (Edges), average 485 degree of network (AvgD), average path length (AvgPL), clustering coefficient (CC), if the 486 network is connected or not (C?) and the minimum cut off (MinCut). 487 Supplementary Table S2: Table of community properties including size and average 488 degree of the three identified communities. The size corresponds to the number of lipids 489 and metabolites (nodes) within each community. The average degree of the community 490 corresponds to how connected lipids and metabolites (nodes) within the concerned 491 community are. 492 Supplementary Table S3: A structural composition table, providing an overview of 493 composition of enriched glycerolipids in the network communities c1 and c2. 494 Supplementary Table S4: Correlation table predicting clinicalvariables based on the 495 community score (FDR < 0.12). Ranked according to FDR (p-value adj). 496 . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 17 Supplementary Table S5: Association table predicting clinical variables based on each 497 lipid and metabolite concentration (FDR < 0.07) within community c1. The model is 498 adjusted for MetS, sex and age. Ranked according to FDR (p-value adj). Five key lipids 499 and one key metabolite were associated with VAT and marked in bold in the table. 500 Supplementary Table S6: Top 10% nodes in community c1 based on the degree. Thus, 501 the most interconnected nodes in the community. The lipids TAG(52:2)-FA(16:0), 502 TAG(52:2)-FA(18:1) and TAG(54:3)-FA(20:3) are marked in bold, as they were among the 503 key lipids. 504 Supplementary Figure S1: Degree distribution for the positive weighted against the 505 random network. 506 Supplementary Figure S2: Ontology enrichment plots for the three communities c1, c2 507 and c3. (a) Ontology enrichment plots for community c1 (blue) and c2 (green). (b) 508 Ontology enrichment plot for community c3. 509 510 511 512 Table 1: Clinical and demographic characteristics compared between PLWH without 513 MetS and PLWH with MetS. P-values in bold indicates a significant difference in the 514 concerned variables between the two groups. 515 516 Variables PLWH without MetS PLWH with MetS pvalue Sa mple (n ) 100 100 Se x, Mal e, n (% ) 90 (90. 0) 90 ( 9 0.0) 1.00 * * Age , mean (sd ) 54.4 ( 9 .5 ) 54.6 (8.5 ) 0.80 * Ethnicity, n (%) 0.87 * * Cauca s ia n 88 (88. 0) 86 ( 8 6.0) As i a n 3 (3.0) 2 (2. 0) Black 4 (4.0) 6 (6. 0) O the r/u nknow n 5 (5.0) 6 (6. 0) Immun ode fici ency , n (%) 14 (14. 0) 13 ( 1 3.0) 1.00 * * Ex posur e t o ea rly-ge n era tion ART, n (%) 34 (34. 0) 46 ( 4 6.0) 0.11 * * VAT, m ean ( sd) 76.1 ( 5 3.6 ) 149 .4 (71) < 1 e-1 1 * SAT , mean (sd ) 111.1 (71.1) 150 .6 (77.1) < 0.001 * ART_NRT I, n (%) 95 (95. 0) 96 ( 9 6.0) 1.00 * * ART_N NRTI, n ( % ) 54 (54. 0) 45 ( 4 5.0) 0.26 * * . CC-BY 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted May 8, 2021. ; https://doi.org/10.1101/2021.05.04.21256640doi: medRxiv preprint 18 ART_P I, n (%) 37 (37. 0) 47 ( 4 7.0) 0.20 * * ART_I N ST I, n (% ) 16 (16. 0) 21 ( 2 1.0) 0.47 * * ART_o the r/unkno wn, n (% ) 0 (0.0) 3 (3. 0) 0.24 * * Mann-Whitney U test and ** Chi-square test 517 518 519 520 521 522 523 524 525 526 Table 2: Identified key lipids and key metabolites. Overview of key lipids and key 527 metabolites with significant differential abundance between PLWH without MetS and 528 PLWH with MetS. Listed in alphabetical order. 529 530 Key lipids Key metabolites DAG (16:0 /18:1 ) 1-ca r bo xyethy li s o l euci ne DAG (16:0 /18:3 ) 4-cho le sten -3 -on e TAG(44:0 ) -F A(18 :0) 4-hydrox yglutam at e TAG(52:2 ) -F A(16 :0) α -ke togl uta r a t e TAG(52:2 ) -F A(18 :1) ca r ote ne diol (2) TAG(52:2 ) -F A(18 :2) γ - g lu t am y l g lu t am a te TAG(52:3 ) -F A(18 :1) glutama te TAG(54:3 ) -F A(16 :0) gly cerate TAG(54:3 ) -F A(20 :2) isol eucine TAG(54:3 ) -F A(20 :3) PC /3- MAP C * TAG(54:4 ) -F A(16 :0) TAG(54:4 ) -F A(20 :3) TAG(54:5 ) -F A(16 :0) PSP * * * pi meloy lcarn i t ine/ 3-methyl adipoylcarnitine (C7 -DC) 531 ** palmi toyl-sphingosine-phosphoet hanolamine (d18:1/16:0) 532 533 534 535

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