Healthy lifestyle behaviors, mediating biomarkers, and risk of microvascular complications among individuals with type 2 diabetes: A cohort study
Open accessAbstract
Background: The influence of overall lifestyle behaviors on diabetic microvascular complications remains unknown. In addition, the potential mediating biomarkers underlying the association is unclear. This study aimed to examine the associations of the combined lifestyle factors with risks of total and individual microvascular complications among patients with type 2 diabetes (T2D) and to explore the potential mediation effects of metabolic biomarkers. Methods and findings: This retrospective cohort study included 15,104 patients with T2D free of macro- and microvascular complications at baseline (2006 to 2010) from the UK Biobank. Healthy lifestyle behaviors included noncurrent smoking, recommended waist circumference, regular physical activity, healthy diet, and moderate alcohol drinking. Outcomes were ascertained using electronic health records. Over a median of 8.1 years of follow-up, 1,296 cases of the composite microvascular complications occurred, including 558 diabetic retinopathy, 625 diabetic kidney disease, and 315 diabetic neuropathy, with some patients having 2 or 3 microvascular complications simultaneously. After multivariable adjustment for sociodemographic characteristics, history of hypertension, glycemic control, and medication histories, the hazard ratios (95% confidence intervals (CIs)) for the participants adhering 4 to 5 low-risk lifestyle behaviors versus 0 to 1 were 0.65 (0.46, 0.91) for diabetic retinopathy, 0.43 (0.30, 0.61) for diabetic kidney disease, 0.46 (0.29, 0.74) for diabetic neuropathy, and 0.54 (0.43, 0.68) for the composite outcome (all Ps -trend ≤0.01). Further, the population-attributable fraction (95% CIs) of diabetic microvascular complications for poor adherence to the overall healthy lifestyle (<4 low-risk factors) ranged from 25.3% (10.0%, 39.4%) to 39.0% (17.7%, 56.8%). In addition, albumin, HDL-C, triglycerides, apolipoprotein A, C-reactive protein, and HbA 1c collectively explained 23.20% (12.70%, 38.50%) of the associations between overall lifestyle behaviors and total diabetic microvascular complications. The key limitation of the current analysis was the potential underreporting of microvascular complications because the cases were identified via electronic health records. Conclusions: Adherence to overall healthy lifestyle behaviors was associated with a significantly lower risk of microvascular complications in patients with T2D, and the favorable associations were partially mediated through improving biomarkers of glycemic control, systemic inflammation, liver function, and lipid profile.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
[37] integrated behavioral surveys with risk perception analysis to assess the gap between perceived and actual preventive behaviors.
“For instance, [23] employed a combination of surveys and clinical assessments to investigate racial disparities in sleep and diabetes risk, while [37] integrated behavioral surveys with risk perception analysis to assess the gap between perceived and actual preventive behaviors.”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [37]
The Methods section (‘Measurements of lifestyle behaviors’; page unavailable) describes questionnaire-derived and self-reported lifestyle data, supporting the behavioral-survey part. It does not describe risk-perception analysis or a comparison between perceived and actual preventive behaviors; the Results instead examine lifestyle behaviors and microvascular complications. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAmong the nine technology-assisted studies, physical activity was the most commonly targeted behavior (67%, 6/9 studies), followed by medication adherence (44%, 4/9), dietary behaviors (33%, 3/9), and self-monitoring of blood glucose (22%, 2/9).
“Among the nine technology-assisted studies, physical activity was the most commonly targeted behavior (67%, 6/9 studies), followed by medication adherence (44%, 4/9), dietary behaviors (33%, 3/9), and self-monitoring of blood glucose (SMBG) (22%, 2/9) [10,20,23], [25,26,30,31,34,37].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [37]
The Methods section, “Measurements of lifestyle behaviors” (page unavailable), evaluates physical activity and diet in a cohort but does not describe technology-assisted studies. Searches for the nine-study count, the reported percentages, medication adherence, and self-monitoring of blood glucose found no passage in our copy addressing those claims. (LLM, conf=0.77)
Experimental — accuracy not yet measuredMixed-methods designs achieved 78% accuracy, 80% sensitivity, and 74% specificity.
“They achieved 78% accuracy, 80% sensitivity, and 74% specificity, and were particularly useful in understanding the motivations behind complex behaviors such as diet and exercise [23,37].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [37]
The Methods sections describe lifestyle scoring and statistical analyses, while the Results report associations with microvascular complications. A search for mixed-methods designs and the stated accuracy, sensitivity, and specificity found no passage reporting those performance figures or an analysis of motivations behind diet and exercise. Page numbers are unavailable in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measuredMixed-methods designs were particularly useful in understanding motivations behind complex behaviors such as diet and exercise.
“They achieved 78% accuracy, 80% sensitivity, and 74% specificity, and were particularly useful in understanding the motivations behind complex behaviors such as diet and exercise [23,37].”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [37]
The Methods (Measurements of lifestyle behaviors) describe questionnaire-based activity measures and a dietary score, while the Statistical analysis section describes quantitative biomarker analyses. Searches for mixed-methods designs, motivations behind diet and exercise, and the cited accuracy, sensitivity, and specificity figures found no passage addressing the claim. (LLM, conf=0.75)
Experimental — accuracy not yet measuredCombining statistical data with participant narratives provided a more comprehensive understanding of behavior change, especially in diet and exercise interventions.
“For example, [37] and [23] demonstrated how combining statistical data with participant narratives provided a more comprehensive understanding of behavior change, especially in diet and exercise interventions.”
From Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review (2025) · cites this paper as [37]
The Methods sections describe questionnaire-derived lifestyle measures and statistical analyses, and the Discussion describes the study as a cohort analysis. Searches for participant narratives, qualitative interviews, and mixed methods found no passage saying that this study combined narratives with statistical data or used that combination to understand behavior change in diet and exercise interventions. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001779 · Cited by 12,728 · see citing works
Compendium of physical activities: an update of activity codes and MET intensities
AINSWORTH, B. E., HASKELL, W. L., WHITT, M. C., IRWIN, M. L., SWARTZ, A. M., STRATH, S. J., O???BRIEN, W. L., BASSETT, D. R., SCHMITZ, K. H., EMPLAINCOURT, P. O., et al. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00009 · Cited by 8,497 · see citing works
Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes
Gæde, P., Vedel, P., Larsen, N., Jensen, G. V. H., Parving, H.-H., & Pedersen, O. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa021778 · Cited by 4,378 · see citing works
DeFronzo, R. A., Ferrannini, E., Groop, L., Henry, R. R., Herman, W. H., Holst, J. J., Hu, F. B., Kahn, C. R., Raz, I., Shulman, G. I., et al. (2015) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2015.19 · Cited by 2,611 · see citing works
Chatterjee, S., Khunti, K., & Davies, M. J. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30058-2 · Cited by 2,577 · see citing works
Esposito, K., Marfella, R., Ciotola, M., Di Palo, C., Giugliano, F., Giugliano, G., D'Armiento, M., D'Andrea, F., & Giugliano, D. (2004) · JAMA · doi:10.1001/jama.292.12.1440 · Cited by 2,126 · see citing works
Effects of a Mediterranean-Style Diet on Cardiovascular Risk Factors: A Randomized Trial
Estruch, R., Martínez-González, M. Á., Corella, D., Salas-Salvadó, J., Ruiz-Gutiérrez, V., Covas, M. I., Fiol, M., Gómez-Gracia, E., López-Sabater, M. C., Vinyoles, E., et al. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-145-1-200607040-00004 · Cited by 1,704 · see citing works
Prevalence of Diabetic Retinopathy in the United States, 2005-2008
Zhang, X., Saaddine, J. B., Chou, C.-F., Cotch, M. F., Cheng, Y. J., Geiss, L. S., Gregg, E. W., Albright, A. L., Klein, B. E. K., & Klein, R. (2010) · JAMA · doi:10.1001/jama.2010.1111 · Cited by 1,196 · see citing works
5. Lifestyle Management:<i>Standards of Medical Care in Diabetes—2019</i>
American Diabetes Association (2019) · Diabetes Care · doi:10.2337/dc19-s005 · Cited by 700 · see citing works
Said, M. A., Verweij, N., & van der Harst, P. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.1717 · Cited by 645 · see citing works
Association of Weight Status With Mortality in Adults With Incident Diabetes
Carnethon, M. R., De Chavez, P. J. D., Biggs, M. L., Lewis, C. E., Pankow, J. S., Bertoni, A. G., Golden, S. H., Liu, K., Mukamal, K. J., Campbell-Jenkins, B., et al. (2012) · JAMA · doi:10.1001/jama.2012.9282 · Cited by 567 · see citing works
Goodpaster, B. H., DeLany, J. P., Otto, A. D., Kuller, L., Vockley, J., South-Paul, J. E., Thomas, S. B., Brown, J., McTigue, K., Hames, K. C., et al. (2010) · JAMA · doi:10.1001/jama.2010.1505 · Cited by 558 · see citing works
Epidemiology and Impact on Quality of Life of Postherpetic Neuralgia and Painful Diabetic Neuropathy
Schmader, K. E. (2002) · Clinical Journal of Pain · doi:10.1097/00002508-200211000-00002 · Cited by 550 · see citing works
Parving, H., Lewis, J., Ravid, M., Remuzzi, G., & Hunsicker, L. (2006) · Kidney International · doi:10.1038/sj.ki.5000377 · Cited by 527 · see citing works
Body-Mass Index and Mortality among Adults with Incident Type 2 Diabetes
Tobias, D. K., Pan, A., Jackson, C. L., O’Reilly, É. J., Ding, E. L., Willett, W. C., Manson, J. E., & Hu, F. B. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1304501 · Cited by 429 · see citing works
Self-citationNight Shift Work, Genetic Risk, and Type 2 Diabetes in the UK Biobank
Vetter, C., Dashti, H. S., Lane, J. M., Anderson, S. G., Schernhammer, E. S., Rutter, M. K., Saxena, R., & Scheer, F. A. J. L. (2018) · Diabetes Care · doi:10.2337/dc17-1933 · Cited by 371 · see citing works
Physical activity, multimorbidity, and life expectancy: a UK Biobank longitudinal study
Chudasama, Y. V., Khunti, K. K., Zaccardi, F., Rowlands, A. V., Yates, T., Gillies, C. L., Davies, M. J., & Dhalwani, N. N. (2019) · BMC Medicine · doi:10.1186/s12916-019-1339-0 · Cited by 353 · see citing works
Chudasama, Y., Khunti, K. K., Gillies, C. L., Dhalwani, N. N., Davies, M. J., Yates, T. E., & Zaccardi, F. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003332 · Cited by 327 · see citing works
(2014) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(14)70156-1 · Cited by 319 · see citing works
Gong, Q., Gregg, E. W., Wang, J., An, Y., Zhang, P., Yang, W., Li, H., Li, H., Jiang, Y., Shuai, Y., et al. (2011) · Diabetologia · doi:10.1007/s00125-010-1948-9 · Cited by 251 · see citing works
Andersen, S. T., Witte, D. R., Dalsgaard, E.-M., Andersen, H., Nawroth, P., Fleming, T., Jensen, T. M., Finnerup, N. B., Jensen, T. S., Lauritzen, T., et al. (2018) · Diabetes Care · doi:10.2337/dc17-2062 · Cited by 248 · see citing works
Johansen, M. Y., MacDonald, C. S., Hansen, K. B., Karstoft, K., Christensen, R., Pedersen, M., Hansen, L. S., Zacho, M., Wedell-Neergaard, A.-S., Nielsen, S. T., et al. (2017) · JAMA · doi:10.1001/jama.2017.10169 · Cited by 244 · see citing works
Ricardo, A. C., Anderson, C. A. M., Yang, W. P., Zhang, X., Fischer, M. J., Dember, L. M., Fink, J. C., Frydrych, A., Jensvold, N. G., Lustigova, E., et al. (2014) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2014.09.016 · Cited by 209 · see citing works
Man, R. E. K., Sabanayagam, C., Chiang, P. P.-C., Li, L., Noonan, J. E., Wang, J. J., Wong, T. Y., Cheung, C. M. G., Tan, G. S. W., & Lamoureux, E. L. (2015) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2015.5103 · Cited by 125 · see citing works
Dunkler, D., Kohl, M., Heinze, G., Teo, K. K., Rosengren, A., Pogue, J., Gao, P., Gerstein, H. C., Yusuf, S., Oberbauer, R. W., et al. (2014) · Kidney International · doi:10.1038/ki.2014.370 · Cited by 122 · see citing works
The Look AHEAD Research Group (2017) · Diabetologia · doi:10.1007/s00125-017-4253-z · Cited by 114 · see citing works
Blomster, J., Zoungas, S., Chalmers, J. P., Li, Q., Chow, C. K., Woodward, M., Mancia, G., Poulter, N. R., Williams, B., Harrap, S. B., et al. (2014) · Diabetes Care · doi:10.2337/dc13-2727 · Cited by 111 · see citing works
Cardiovascular and Kidney Outcomes Across the Glycemic Spectrum
Honigberg, M. C., Zekavat, S. M., Pirruccello, J. P., Natarajan, P., & Vaduganathan, M. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.05.004 · Cited by 108 · see citing works
United Kingdom Biobank (UK Biobank)
Caleyachetty, R., Littlejohns, T. J., Lacey, B., Bešević, J., Conroy, M. C., Collins, R., & Allen, N. E. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.03.342 · Cited by 107 · see citing works
Blomster, J. I., Chow, C. K., Zoungas, S., Woodward, M., Patel, A., Poulter, N. R., Marre, M., Harrap, S., Chalmers, J., & Hillis, G. S. (2013) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.12122 · Cited by 86 · see citing works
Bonaccio, M., Costanzo, S., Castelnuovo, A. F. D., Persichillo, M., Magnacca, S., Curtis, A. D., Cerletti, C., Donati, M. B., de Gaetano, G., Iacoviello, L., et al. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab783 · Cited by 77 · see citing works
King, C. C., Piper, M. E., Gepner, A. D., Fiore, M. C., Baker, T. B., & Stein, J. H. (2016) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.116.308728 · Cited by 72 · see citing works
Franco, I., Bianco, A., Mirizzi, A., Campanella, A., Bonfiglio, C., Sorino, P., Notarnicola, M., Tutino, V., Cozzolongo, R., Giannuzzi, V., et al. (2020) · Nutrients · doi:10.3390/nu13010066 · Cited by 69 · see citing works
Man, R. E. K., Gan, A. T. L., Fenwick, E., Gupta, P., Wong, M. Y. Z., Wong, T. Y., Tan, G. S. W., Teo, B. W., Sabanayagam, C., & Lamoureux, E. L. (2018) · Nutrients · doi:10.3390/nu10111685 · Cited by 51 · see citing works
Dallongeville, J., Bhatt, D. L., Steg, P. G., Ravaud, P., Wilson, P. W. F., Eagle, K. A., Goto, S., Mas, J., Montalescot, G., & (for the REACH Registry Investigators) (2011) · European Journal of Preventive Cardiology · doi:10.1177/1741826710394305 · Cited by 49 · see citing works
Tracey, M., McHugh, S., Fitzgerald, T., Buckley, C. M., Canavan, R. J., & Kearney, P. M. (2016) · Journal of Diabetes Research · doi:10.1155/2016/5975903 · Cited by 46 · see citing works
High FIB4 index is an independent risk factor of diabetic kidney disease in type 2 diabetes
Saito, H., Tanabe, H., Kudo, A., Machii, N., Higa, M., Yamaguchi, S., Maimaituxun, G., Abe, K., Takahashi, A., Tanaka, K., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-88285-6 · Cited by 44 · see citing works
Dunkler, D., Kohl, M., Teo, K. K., Heinze, G., Dehghan, M., Clase, C. M., Gao, P., Yusuf, S., Mann, J. F. E., & Oberbauer, R. W. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv086 · Cited by 41 · see citing works
Association between a Healthy Lifestyle Score and inflammatory markers among Puerto Rican adults
Sotos‐Prieto, M., Bhupathiraju, S. N., Falcón, L. M., Gao, X., Tucker, K. L., & Mattei, J. (2015) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2015.12.004 · Cited by 36 · see citing works
Schrauben, S. J., Hsu, J. Y., Amaral, S. G., Anderson, A. H., Feldman, H. I., & Dember, L. M. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020040394 · Cited by 35 · see citing works
Shi, N., Aroke, D., Qi, J., Lee, D. H., Hussan, H., Zhang, X., Manson, J. E., LeBlanc, E. S., Barac, A., Arcan, C., et al. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.690428 · Cited by 34 · see citing works
Figueras‐Roca, M., Molins, B., Sala‐Puigdollers, A., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173865 · Cited by 31 · see citing works
Nivukoski, U., Niemelä, M., Bloigu, A. H., Bloigu, R., Aalto, M., Laatikainen, T. K., & Niemelä, O. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0218463 · Cited by 29 · see citing works
Lifestyle Score and Genetic Factors With Hypertension and Blood Pressure Among Adults in Rural China
Niu, M., Zhang, L., Wang, Y., Tu, R., Liu, X., Wang, C., & Bie, R. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.687174 · Cited by 25 · see citing works
Combined effects of lifestyle risk factors on fatty liver index
Nivukoski, U., Niemelä, M., Bloigu, A. H., Bloigu, R., Aalto, M., Laatikainen, T. K., & Niemelä, O. J. (2020) · BMC Gastroenterology · doi:10.1186/s12876-020-01270-7 · Cited by 24 · see citing works
Healthful dietary patterns and risk of end-stage kidney disease: the Singapore Chinese Health Study
Geng, T., Jafar, T. H., Neelakantan, N., Yuan, J., van Dam, R. M., & Koh, W. (2020) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqaa348 · Cited by 22 · see citing works
Self-citationGeng, T.-T., Talaei, M., Jafar, T., Yuan, J.-M., & Koh, W. (2019) · Journal of the American Heart Association : Cardiovascular and Cerebrovascular Disease · doi:10.1161/jaha.119.013282 · Cited by 16 · see citing works
Self-citationTu, Z., Lu, Q., Zhang, Y., Shu, Z., Lai, Y., Ma, M., Xia, P., Geng, T., Chen, J., Li, Y., et al. (2022) · Engineering · doi:10.1016/j.eng.2022.04.010 · Cited by 13 · see citing works
Self-citationParamastri, R., Hsu, C., Chuang, Y., Lee, H.-A., Wiratama, B. S., & Chao, J. C.-J. (2021) · Nutrients · doi:10.3390/nu13103591 · Cited by 7 · see citing works
Geng, T.-T., Jafar, T., Yuan, J.-M., & Koh, W. (2020) · Journal of Diabetes · doi:10.1111/1753-0407.13034 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [1]IDF. International Diabetes Federation Diabetes Atlas. 10th edition. Brussels; 2021.
- [36]Biobank UK. UK Biobank Biomarker Project—Companion Document to Accompany Serum Biomarker Data. UK Biobank Showcase. 2019; 1(2019): 2019.
Cited by
Sorted by citations
Kristensen, F. P. B., Sanchez-Lastra, M. A., Dalene, K. E., del Pozo Cruz, B., Ried-Larsen, M., Thomsen, R. W., Ding, D., Ekelund, U., & Tarp, J. (2023) · Diabetes Care · doi:10.2337/dc23-0937 · Cited by 29 · see citing works
Methodological Approaches in Studying Type-2 Diabetes-Related Health Behaviors—A Systematic Review
Khandoker, F., & Grigsby, T. J. (2025) · Applied Sciences · doi:10.3390/app151910567 · Cited by 2 · see citing works
- Authors
- Geng, T., Zhu, K., Lu, Q., Wan, Z., Chen, X., Liu, L., Pan, A., & Liu, G.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Pan, An
- Liu, Gang
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2023-01-10
- Volume
- 20
- Issue
- 1
- Pages
- e1004135
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC9831321
- OpenAlex
- W4315436518
- Cited by
- 205
- References
- 50
- Citations by year
- 2026: 46 · 2025: 70 · 2024: 69 · 2023: 20
- FWCI
- 42.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (82073554, 81930124, 82021005, and 82192902)
- Science Fund for Distinguished Young Scholars of Hubei Province (2021CFA048)
- Fundamental Research Funds for the Central Universities (2021GCRC076, 2021GCRC075)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- type 2 diabetes, healthy lifestyle behaviors, microvascular complications, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, mediation analysis, metabolic biomarkers, physical activity, waist circumference, alcohol consumption, diet, smoking, population attributable fraction, albumin, C-reactive protein, apolipoprotein(a), HDL cholesterol, triglycerides, HbA1c
- MeSH terms
- Healthy Lifestyle, Diabetes Mellitus, Type 2, Diabetic Nephropathies, Diabetic Neuropathies, Diabetic Retinopathy, Humans, Retrospective Studies, Risk Factors, Cohort Studies, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
