Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns
BackgroundHarmful drinking habits can have a profound effect on individual health. However, there is currently a lack of network analysis studies on clinical indicators related to drinking population. The aim of this study was to investigate the relationships among drinking characteristics, cognitiv...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1553691/full |
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| author | Shuqi Xu Shuqi Xu Shuqi Xu Shuqi Xu Ranran Zhao Ranran Zhao Ranran Zhao Jincheng Wang Jincheng Wang Jincheng Wang Xue Yang Xue Yang Xue Yang Lan Wang Lan Wang Lan Wang Cuixia An Cuixia An Cuixia An Xueyi Wang Xueyi Wang Xueyi Wang Ran Wang Ran Wang Ran Wang |
| author_facet | Shuqi Xu Shuqi Xu Shuqi Xu Shuqi Xu Ranran Zhao Ranran Zhao Ranran Zhao Jincheng Wang Jincheng Wang Jincheng Wang Xue Yang Xue Yang Xue Yang Lan Wang Lan Wang Lan Wang Cuixia An Cuixia An Cuixia An Xueyi Wang Xueyi Wang Xueyi Wang Ran Wang Ran Wang Ran Wang |
| author_sort | Shuqi Xu |
| collection | DOAJ |
| description | BackgroundHarmful drinking habits can have a profound effect on individual health. However, there is currently a lack of network analysis studies on clinical indicators related to drinking population. The aim of this study was to investigate the relationships among drinking characteristics, cognitive functions, liver and kidney functions, and glucose and lipid levels in alcohol drinkers through the application of network analysis.MethodWe conducted a stratified random sampling survey of 1,432 male employees in Gaocheng District, Hebei Province, in 2016. The Alcohol Dependence Scale (ADS) and the Alcohol Use Disorders Identification Test (AUDIT) were utilized to evaluate alcohol-related behaviors. Cognitive functions were assessed via the Hopkins Verbal Learning Test (HVLT), the Brief Visuospatial Memory Test (BVMT), Digit Symbol Coding Test (DSCT), and Digit Span Test (DST). Additionally, biochemical indicators such as blood glucose and lipid levels and hepatic and renal functions were measured. Analyses were performed to identify central symptoms and bridge symptoms of this network.ResultsIn our network analysis, the nodes representing TC, AST, AST/ALT, and ALT had the highest strength centrality. TC and AST presented the highest expected influence centrality. The closeness centrality indices for all the indicators performed well. The node DSCT ranked highly in terms of betweenness centrality.ConclusionCorrelations may exist among cognitive function, glycemic and lipid profiles, and hepatic–renal function in individuals with varying alcohol consumption patterns. Lipid and liver function indicators were identified as the most central factors in the network model. In the clinic, practitioners may focus on these abnormal central indicators as potential intervention targets to enhance the quality of life in alcohol drinkers. |
| format | Article |
| id | doaj-art-127d449d538542b79b8975e55e59e2ad |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-127d449d538542b79b8975e55e59e2ad2025-08-20T03:34:33ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-07-011610.3389/fendo.2025.15536911553691Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patternsShuqi Xu0Shuqi Xu1Shuqi Xu2Shuqi Xu3Ranran Zhao4Ranran Zhao5Ranran Zhao6Jincheng Wang7Jincheng Wang8Jincheng Wang9Xue Yang10Xue Yang11Xue Yang12Lan Wang13Lan Wang14Lan Wang15Cuixia An16Cuixia An17Cuixia An18Xueyi Wang19Xueyi Wang20Xueyi Wang21Ran Wang22Ran Wang23Ran Wang24Department of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaDepartment of Psychiatry, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaMental Health Center, Hebei Medical University, Shijiazhuang, Hebei, ChinaHebei Clinical Research Center of Mental Disorders, Institute of Mental Health, Shijiazhuang, Hebei, ChinaBackgroundHarmful drinking habits can have a profound effect on individual health. However, there is currently a lack of network analysis studies on clinical indicators related to drinking population. The aim of this study was to investigate the relationships among drinking characteristics, cognitive functions, liver and kidney functions, and glucose and lipid levels in alcohol drinkers through the application of network analysis.MethodWe conducted a stratified random sampling survey of 1,432 male employees in Gaocheng District, Hebei Province, in 2016. The Alcohol Dependence Scale (ADS) and the Alcohol Use Disorders Identification Test (AUDIT) were utilized to evaluate alcohol-related behaviors. Cognitive functions were assessed via the Hopkins Verbal Learning Test (HVLT), the Brief Visuospatial Memory Test (BVMT), Digit Symbol Coding Test (DSCT), and Digit Span Test (DST). Additionally, biochemical indicators such as blood glucose and lipid levels and hepatic and renal functions were measured. Analyses were performed to identify central symptoms and bridge symptoms of this network.ResultsIn our network analysis, the nodes representing TC, AST, AST/ALT, and ALT had the highest strength centrality. TC and AST presented the highest expected influence centrality. The closeness centrality indices for all the indicators performed well. The node DSCT ranked highly in terms of betweenness centrality.ConclusionCorrelations may exist among cognitive function, glycemic and lipid profiles, and hepatic–renal function in individuals with varying alcohol consumption patterns. Lipid and liver function indicators were identified as the most central factors in the network model. In the clinic, practitioners may focus on these abnormal central indicators as potential intervention targets to enhance the quality of life in alcohol drinkers.https://www.frontiersin.org/articles/10.3389/fendo.2025.1553691/fullprimary funding cognitive functionglycemiclipid profileshepatic-renal functiondrinking populationnetwork analysis research data not shared |
| spellingShingle | Shuqi Xu Shuqi Xu Shuqi Xu Shuqi Xu Ranran Zhao Ranran Zhao Ranran Zhao Jincheng Wang Jincheng Wang Jincheng Wang Xue Yang Xue Yang Xue Yang Lan Wang Lan Wang Lan Wang Cuixia An Cuixia An Cuixia An Xueyi Wang Xueyi Wang Xueyi Wang Ran Wang Ran Wang Ran Wang Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns Frontiers in Endocrinology primary funding cognitive function glycemiclipid profiles hepatic-renal function drinking population network analysis research data not shared |
| title | Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns |
| title_full | Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns |
| title_fullStr | Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns |
| title_full_unstemmed | Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns |
| title_short | Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns |
| title_sort | network analysis of cognitive function glycemic lipid profiles and hepatic renal function in individuals with diverse drinking patterns |
| topic | primary funding cognitive function glycemiclipid profiles hepatic-renal function drinking population network analysis research data not shared |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1553691/full |
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