Type 2 diabetes (T2D) is one of the most prevalent metabolic conditions worldwide, 59 and 463 million adults are living with diabetes. 1 To examine lifestyle-related risk 60 factors for the development of T2D, the role of different fatty acids has been an 61 important research agenda. During the past two decades, consumption of n-6 62 polyunsaturated fatty acids (PUFAs), rich in vegetable oil, has been increasing 63 rapidly, 2 3 while their relationship with human health remains controversial. The 64 metabolic pathway of n-6 PUFAs is hypothesized to be closely involved in the T2D 65 etiology, 4 but has not been well characterized yet. 66 67 A recent pooled analysis of 20 prospective cohort studies investigated the association 68 of linoleic acid ((LA; 18:2n6) and arachidonic acid (AA; 20:4n6) biomarkers with 69 incident T2D, reporting that LA, but not AA, was inversely associated with T2D. 5 70 However, in this pooling project, only two individual n-6 PUFA biomarkers were 71 evaluated while cohorts in Europe reported gamma-linolenic acid (GLA) and other 72 n-6 PUFAs to be associated with higher T2D risk. 5 Moreover, with only one small 73 cohort from Taiwan in the above pooling project, 5 evidence from Asian populations 74 has been limited. As an Asian population has different metabolic and lifestyle 75 characteristics in relation to diabetes compared to American or European 76 populations, 6 more investigation for n-6 PUFA biomarkers in Asian populations is 77 highly warranted. 78 79 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 The protective effect of n-6 PUFA or LA, the most abundant n-6 PUFA, on insulin 80 homeostasis has been well characterized and discussed to link n-6 PUFA exposure to 81 lower T2D risk. 7 8 However, gut microbiota has been little investigated in research on 82
PUFAs and any non-communicable diseases. On one hand, gut microbiota has been 83 considered as an important risk factor for T2D and insulin resistance. 9 10 On the other, 84 rodent studies have suggested that high tissue levels of n-6 PUFAs are associated with 85 differences in gut microbiota, including increased Proteobacteria, reduced 86
Actinobacteria and Bacteroidetes, and altered microbial α -diversity. [11] [12] [13] Yet, to our 87 knowledge, there are no cohort studies investigating the potential link between n-6 88 PUFA exposure, T2D risk, and gut microbiota composition and diversity. 89 90 Evaluating the prospective cohort in China, we aimed to examine the prospective 91 associations 1) between n-6 PUFA biomarkers and T2D risk in a Chinese population, 92
and 2) between n-6 PUFA biomarkers and gut microbiota composition and diversity, 93 and subsequent health outcomes. 94
This study was based on the Guangzhou Nutrition and Health Study (GNHS), a 98 community-based prospective cohort in southern China. Detailed information of the 99 participants and study design was described previously. 14 Briefly, 4,048 Chinese 100 participants aged 40-75 years and lived in Guangzhou for at least 5 years were 101 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https: //doi.org/10.1101 //doi.org/10. /2020 recruited between 2008-2010 (n=3,169) and 2012-2013 (n=879) . All participants have 102 been followed up every 3 years approximately. During each visit, demographic, 103 dietary and lifestyle information was collected via questionnaires. The study was 104 registered at clinicaltrials.gov (NCT03179657). 105 106 According to our pre-specified inclusion and exclusion criteria of our participants for 107 the present analysis ( Figure S1 ), we excluded participants with missing information 108 on age or sex (n=9), diet (n=7), self-reported/diagnosed T2D (n=400) or history of 109 cancer (n=16) at baseline, or extreme levels of self-reported total energy intake (<800 110 kcal or >4,000 kcal per day for men, and <500 kcal or > 3,500 kcal per day for 111 women) (n=53). We also excluded those without measurement of erythrocyte 112 membrane fatty acid compositions at baseline (n=298) and further excluded those 113 without any follow-up information on T2D status (n=534, follow-up rate 84%). The 114 current analyses were censored by date of T2D ascertainment or Apr 30, 2019, which 115 ever happened first. A total of 2,731 individuals were included in the present study, 116 with a median follow-up time of 6.2 years. 117 118 Among the above included 2,731 individuals, stool samples from 1,606 individuals 119 were collected between 2014-2018 for the subsequent 16S profiling, with 1,591 120 individuals included in the microbial association analysis after excluding participants 121 who used antibiotics within two weeks before stool collection (n=15). 122 123 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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We considered relative concentrations (% of total fatty acids) of erythrocyte LA, GLA, 125 and AA as the main exposure variables. Blood samples were drawn after overnight 126 fasting at the baseline visit. Erythrocytes were aliquoted within 2 hours of blood 127 sampling and stored at -80°C. As previously described, 15 fatty acid moieties of 128 erythrocyte membranes were trans-methylated and measured as proportions (%) of 129 total fatty acids by using gas chromatography (7890 GC, DB-23 capillary column 130 60m×0.25mm internal diameter×0.15μm film, Agilent, California, USA). 131
Commercially available standards (Nu-Chek Prep, Minnesota, USA) were used to 132 identify individual fatty acids (n=37) and quantify a relative peak strength of each. 133
The intra-assay coefficients of variation for LA, GLA and AA were 6.4%, 12.8%, and 134 (version 3.6.3). We used multivariable Poisson regression models to estimate the risk 157 ratio (RR) and 95% confidence interval (CI) of T2D comparing quartiles of the 158 erythrocyte n-6 PUFA (LA, GLA and AA), adjusting for potential confounders. We 159 fitted three statistical models: model 1 included age, sex, BMI, and waist-hip ratio 160 (WHR); model 2, model 1 + education, household income, smoking, alcohol drinking 161 status, physical activity, total energy intake, and family history of diabetes; and model 162 3, as model 2 + baseline erythrocyte total n-3 PUFAs and fasting glucose. p-value for 163 trend was calculated based on per quartile difference in the corresponding n-6 PUFA 164 variable. 165
In sensitivity analyses, we repeated the above analysis based on model 3 to examine 167 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 the impact of loss to follow up by a simple imputation (assuming that participants loss 168 to follow up did not develop T2D), and 10 rounds of multiple imputations based on 169 regression model using all covariates listed in model 3. 170
We assessed influences of additional potential covariates on the models: dietary 172 variables (dietary intake of dairy products, red and processed meat, fish, vegetable, 173 and fruit, in quartiles), blood lipids (total triglycerides and low-density lipoprotein), 174 prevalent coronary heart disease, treatment for hypertension or hyperlipidemia. We 175 further examined the potential interaction of erythrocyte n-6 PUFA biomarkers with 176 age, sex, BMI or n-3 PUFA biomarker concentrations on T2D risk. Post-hoc stratified 177 analyses were performed if there was a significant interaction (p interaction <0.05) for age, 178 sex, BMI or n-3 PUFA biomarker concentrations with the n-6 PUFA biomarkers. 179
For the gut microbiome analysis, we calculated the α -diversity metrics (observed 181
OTUs, Shannon's diversity index, Chao index and Simpson index) by using QIIME 182 (version 1.9.0) based on rarefied OTU counts. We standardized α -diversity indicators 183 (Z-score was calculated: the variable was subtracted by the mean and divided by the 184 standard deviation) and then reduced assay-dependent variabilities by fitting a linear 185 mixed model with an α -diversity indicator as an outcome variable and with 186 assay-specific covariates including sequencing depth and Bristol scale as fixed effects, 187 and 188 sequencing batch as a random effect. The residuals from this modeling were then used 189 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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in the following n-6 PUFA biomarker association analyses. We used a linear 190 regression model to examine the association of individual n-6 PUFA biomarkers (by 191 quartile) with the four α -diversity metrics, adjusted for age, sex, BMI, waist-hip ratio, 192 education, household income, smoking status, alcohol drinking status, physical 193 activity, total energy intake, and baseline erythrocyte total n-3 PUFAs. We also 194 conducted an additional adjustment for dietary fiber intake to assess the potential 195 marginal effect of dietary fiber, as fiber is a well-known factor influencing the gut 196 microbiome. 19 To investigate the relevance of significant findings in relation to T2D, 197 we performed mediation analysis (R {mediation}) 20 with the residuals of (β-diversity) at genus level by quartiles of n-6 PUFA biomarkers. The potential 208 confounders included in the PERMANOVA were sequencing depth, sequencing 209 batch, Bristol scale, age, sex, BMI, WHR, education, household income, smoking 210 status, alcohol drinking status, physical activity, total energy intake, and baseline 211 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.29.20039693 doi: medRxiv preprint erythrocyte total n-3 PUFAs. The above analysis was repeated for gut composition at 212 OTU level. Scaled relative abundances (i.e., divided by the standard deviation) were 213 used to lessen the influence of highly abundant genera on Bray-Curtis distance. The 214 association of community structure with T2D was also assessed by PERMANOVA (R 215 function adonis, 999 permutations) to obtain R 2 indicating the proportion of the 216 variability explained by the studied variables including n-6 PUFAs. We performed the 217 post-hoc pairwise comparisons between different quartiles of GLA with Bonferroni 218 correction (cut-off p<0.008). 219 220 At genus level, we performed a linear discrimination analysis (LDA) as implemented 221
using LEfSe. 22 The default parameters were used (α value for Wilcoxon tests was 0.05 222 and the LDA score was 2.0) to identify biomarkers at genus level distinguishing 223 different quartiles of n-6 PUFAs. Only taxon present in at least 10% of samples were 224 included in the analyses. Then, we compared the relative abundances of genera by 225 T2D status using the LEfSe method. 22 Besides, we evaluated the potential correlation 226 of the above identified genera with T2D-related traits, including fasting insulin, 227 glucose, total cholesterol, total triglycerides, low-density lipoprotein, high-density 228 lipoprotein cholesterol (HDL), non-HDL, HbA1c, homeostasis model assessment of 229 insulin resistance (HOMA-IR) and β -cell function (HOMA-β). We first modeled 230 T2D-related traits as outcome variables in linear regression with age, sex BMI and 231 obtained the residuals of each T2D-related trait respectively, then we calculated the 232 Spearman correlation coefficients between the genera and the residuals of 233 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.29.20039693 doi: medRxiv preprint T2D-related traits. Results displaying a p<0.05 after Bonferroni adjustment were 234 considered as significant. 235
Characteristics of the study participants 238 Table 1 and Table S1 summarize the baseline characteristics of the population by 239 quartiles of erythrocyte GLA, LA and AA. The median levels (interquartile range) of 240 baseline erythrocyte LA, GLA, AA and total n-6 PUFAs (% total fatty acids) were 241 9.84 (8.89-10.76), 0.037 (0.025-0.052), 11.51 (10.00-12.60) and 21.6 (19.6-23.1), 242 respectively. Table S2 and S3 present baseline population characteristics of 243 participants with and without follow-up information, 16S profiling respectively. 244
Participants who dropped out had higher serum glucose levels, consumed fewer 245 vegetables and energy, tended to be older, less educated and physically active, and 246 were more likely to smoke. 247 248
Higher levels of baseline GLA were associated with higher risk of T2D across the 250 three statistical models (p-trend < 0.001 for all models, association was found for LA or AA. Similar results were obtained after using the 254 simple or multiple imputation for the missing data in the sensitivity analysis (Table  255 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.29.20039693 doi: medRxiv preprint S4). Adjustment for additional dietary factors and disease histories showed similar 256 results (Table S5) . We did not observe significant interaction for n-6 PUFA 257 biomarkers with age, sex, BMI or n-3 PUFAs on T2D risk (p interaction >0.05). 258
The prospective association of erythrocyte n-6 PUFA biomarkers with gut 260 microbiota diversity 261
Lower GLA was associated with higher gut microbiota α -diversity (Q4 vs. Q1: 262 p=0.021 for observed OTUs, 0.028 for Shannon's diversity index, 0.023 for Chao 263 index, respectively) ( Figure 1 ). However, baseline LA, AA or total n-6 PUFAs were 264 not associated with the microbial α -diversity (Table S6 ). These results remained 265 similar after further adjusting for dietary fiber intake (Table S7) . Among 1,591 266 individuals included in the microbial association analysis, we also observed positive 267 association between GLA and T2D (Table S8) . α -diversity acted as a potential partial 268 mediator of the association between GLA and T2D (Table 3) . For β -diversity, GLA 269 and AA significantly (p<0.01) contributed to dissimilarities in gut microbiota 270 composition at genus level (Table S9) . Specifically, for GLA, the gut microbiota 271 composition at genus level was different across quartile groups of GLA 272 concentrations (Table S10) CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.29.20039693 doi: medRxiv preprint diversity index, Simpson index and Chao index compared with patients without T2D 278 (Table S11 ). We also found that the overall β -diversity was different between 279 participants with and without T2D (Table S12) . (Table S13) . 296
In the present community-based prospective cohort of a Chinese population, we found 299 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 that baseline erythrocyte GLA levels were positively associated with incident T2D, 300 independent of BMI and other potential confounders. Proportions of LA, AA or total 301 n-6 PUFAs were not associated with T2D incidence. Our further investigation 302 integrating gut microbiota data revealed that GLA may be associated with T2D risk 303 through a mechanism which varies diversity and composition of gut microbiota. Our 304 findings suggest that n-6 PUFAs and gut microbiota co-vary in the development of 305 T2D risk, highlighting the presence of a novel mechanism of how fatty acids or gut 306 microbiota influence T2D risk. 307 308 Our findings suggest that high circulating GLA might be a risk factor of T2D, which 309 are consistent with several prospective studies in western countries. 23 24 The null 310 finding for AA in our present study is also consistent with those from the previous 311 studies. 5 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 322 Given the putative relationship between gut microbiome and T2D, we hypothesized 323 and confirmed that gut microbiome diversity may play a role linking n-6 PUFAs and 324 T2D and that n-6 PUFAs may prospectively affect gut microbiota composition. Our 325 findings are in line with a randomized controlled trial in which α -diversity, Blautia 326 was reduced, while Bacteroides were increased after higher-fat diet intervention (rich 327 in n-6 PUFAs owing to exclusive use of soybean oil). 26 Although we did not observe 328 higher relative abundance of Bacteroides in T2D patients in our study, Bacteroides 329 has been found to be more abundant in T2D in another Chinese population. 9 330 331 We found that Butyrivibrio, Blautia, Oscillospira, and Odoribacter, genera known to 332 contain a series of butyrate-producing bacteria, are all enriched in participants within 333 Q1 of GLA and participants without T2D. Butyrivibrio fibrisolvens was reported to 334 have the ability to utilize fructose polymers and it showed anticarcinogenic effect in 335 mice. 27 28 Blautia was reported to have a lower relative abundance in T2D patients 336 and have inverse association with visceral fat. 29 30 Oscillospira was positively 337 associated with leanness and was reduced in inflammatory diseases. 31 In addition, 338 evidence suggested that some species of Oscillospira were able to utilize glucuronate, 339 a common animal-derived sugar. 32 Odoribacter splanchnicus was considered as a 340 potential probiotics candidate for obesity prevention as a higher abundance in 341 obesity-depleted group. 33 Moreover, in our study, Blautia, Oscillospira and 342
Odoribacter were inversely associated with circulating triglycerides, which was found 343 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 to be positively associated with T2D and higher level of GLA. 34 35 Thus, our study 344 suggests that lower level of GLA may be correlated with factors increasing the 345 abundance of these bacteria and preventing the development of T2D. 346
The more detailed mechanism behind these associations remains unclear but may be 348 related to chronic inflammation. Accumulating evidence supports the hypothesis that 349 chronic low-grade inflammation is a risk factor for the development of T2D, 36 Strengths of the present study include the prospective cohort study design, which is 365 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.29.20039693 doi: medRxiv preprint rarely conducted in prior research on fatty acid-gut microbiota associations, and the 366 collection and adjustment for a variety of information including socio-demographic, 367 lifestyle and dietary factors, anthropometric parameters and circulating blood 368 biomarkers. Several limitations merit attention. First, although we have adjusted for 369 several potential confounders, due to the observational nature of the present study, we 370 are not able to fully exclude the influence of residual confounding. Second, 371 erythrocyte n-6 PUFAs were measured at baseline only and may not represent 372 long-term status. Third, fecal samples were only collected during follow-up but not at 373 baseline. Repeated measure of both circulating n-6 PUFAs and gut microbiota or an 374 intervention of n-6 PUFAs would enable us to characterize bidirectional effects of n-6 375 PUFA intakes on gut microbiota and of gut microbiota on circulating n-6 PUFAs. 376
Nevertheless, our study fills the gap of the prospective associations between n-6 377 PUFA biomarkers and gut microbiota. 378
In conclusion, our study suggests that erythrocyte GLA biomarker is positively 380 associated with incident T2D in the Chinese population. Gut microbiota may play an 381 important role to explain the finding. The identification of GLA-related gut 382 microbiota features may serve as potential intervention targets for further 383 investigation. The detailed mechanism behind the association of GLA with T2D risk 384 and gut microbiota needs further clarification in the future. 385 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 participants without T2D; red: participants with T2D). Abbreviation: Q, quartile; 603 T2D, type 2 diabetes; Non-T2D, participants without type 2 diabetes. 604 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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