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1. Life expectancy associated with different ages at diagnosis of type 2 diabetes in high-income countries:23 million person-years of observation

Author

Sun, L., Best, L. G., Cooper, C., Bakker, S. J.L., Kavousi, M., Meyer, H. E., Thompson, S. G., Hunt, K. J., Bom, M. T., Kraft, P., Psaty, B. M., de Boer, R., Trompet, S., Tilly, M., Ikram, M. A., Dorr, M., Can, G., Geleijnse, M., Sun, L., Best, L. G., Cooper, C., Bakker, S. J.L., Kavousi, M., Meyer, H. E., Thompson, S. G., Hunt, K. J., Bom, M. T., Kraft, P., Psaty, B. M., de Boer, R., Trompet, S., Tilly, M., Ikram, M. A., Dorr, M., Can, G., and Geleijnse, M.

Abstract

Background:The prevalence of type 2 diabetes is increasing rapidly, particularly among younger age groups. Estimates suggest that people with diabetes die, on average, 6 years earlier than people without diabetes. We aimed to provide reliable estimates of the associations between age at diagnosis of diabetes and all-cause mortality, cause-specific mortality, and reductions in life expectancy. Methods:For this observational study, we conducted a combined analysis of individual-participant data from 19 high-income countries using two large-scale data sources: the Emerging Risk Factors Collaboration (96 cohorts, median baseline years 1961–2007, median latest follow-up years 1980–2013) and the UK Biobank (median baseline year 2006, median latest follow-up year 2020). We calculated age-adjusted and sex-adjusted hazard ratios (HRs) for all-cause mortality according to age at diagnosis of diabetes using data from 1 515 718 participants, in whom deaths were recorded during 23·1 million person-years of follow-up. We estimated cumulative survival by applying age-specific HRs to age-specific death rates from 2015 for the USA and the EU. Findings: For participants with diabetes, we observed a linear dose–response association between earlier age at diagnosis and higher risk of all-cause mortality compared with participants without diabetes. HRs were 2·69 (95% CI 2·43–2·97) when diagnosed at 30–39 years, 2·26 (2·08–2·45) at 40–49 years, 1·84 (1·72–1·97) at 50–59 years, 1·57 (1·47–1·67) at 60–69 years, and 1·39 (1·29–1·51) at 70 years and older. HRs per decade of earlier diagnosis were similar for men and women. Using death rates from the USA, a 50-year-old individual with diabetes died on average 14 years earlier when diagnosed aged 30 years, 10 years earlier when diagnosed aged 40 years, or 6 years earlier when diagnosed aged 50 years than an individual without diabetes. Using EU death rates, the corresponding estimates

Published
2023

6. Blood-based DNA methylation biomarkers for cumulative lead exposure: associations with cardiovascular disease incidence and mortality in the Strong Heart Study

Author

Gao, X., primary, Domingo-Relloso, A., additional, Colicino, E., additional, Cole, S. A., additional, Haack, K., additional, Best, L. G., additional, Umans, J. G., additional, Fallin, M. D., additional, Tellez-Plaza, M., additional, Baccarelli, A. A., additional, and Navas-Acien, A., additional

Published
2020
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10. Covariate-adjusted measures of discrimination for survival data

Author

White, Ian R, Rapsomaniki, Eleni, Wannamethee, S. G., Morris, R. W., Willeit, J., Willeit, P., Santer, P., Kiechl, S., Wald, N., Ebrahim, S., Lawlor, D. A., Gallacher, J., Yarnell, J. W. G., Ben Shlomo, Y., Casiglia, Edoardo, Tikhonoff, V., Sutherland, S. E., Nietert, P. J., Keil, J. E., Bachman, D. L., Psaty, B. M., Cushman, M., Nordestgaard, B. G., Tybjærg Hansen, A., Frikke Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Pilotto, L., Vanuzzo, D., Simons, L. A., Friedlander, Y., Mccallum, J., Price, J. F., Mclachlan, S., Taylor, J. O., Guralnik, J. M., Wallace, R. B., Kohout, F. J., Cornoni Huntley, J. C., Blazer, D. G., Phillips, C. L., Wareham, N. J., Khaw, K. T., Brenner, H., Schöttker, B., Müller, H. T., Rothenbacher, D., Nissinen, A., Donfrancesco, C., Harald, K., Jousilahti, P. R., Vartiainen, E., Salomaa, V., D'Agostino, R. B., Wolf, P. A., Vasan, R. S., Daimon, M., Oizumi, T., Kayama, T., Kato, T., Chetrit, A., Dankner, R., Lubin, F., Welin, L., Svärdsudd, K., Eriksson, H., Lappas, G., Lissner, L., Mehlig, K., Björkelund, C., Nagel, D., Kiyohara, Y., Arima, H., Ninomiya, T., Hata, J., Rodriguez, B., Dekker, J. M., Nijpels, G., Stehouwer, C. D. A., Iso, H., Kitamura, A., Yamagishi, K., Noda, H., Goldbourt, U., Kauhanen, J., Salonen, J. T., Tuomainen, T. P., Meade, T. W., Destavola, B. L., Blokstra, A., Verschuren, W. M. M., de Boer, I. H., Folsom, A. R., Koenig, W., Meisinger, C., Peters, A., Bueno de Mesquita, H. B., Rosengren, A., Wilhelmsen, L., Kuller, L. H., Grandits, G., Cooper, J. A., Bauer, K. A., Davidson, K. W., Kirkland, S., Shaffer, J. A., Shimbo, D., Sato, S., Dullaart, R. P. F., Bakker, S. J. L., Gansevoort, R. T., Ducimetiere, P., Amouyel, P., Arveiler, D., Evans, A., Ferrières, J., Schulte, H., Assmann, G., Jukema, J. W., Westendorp, R. G. J., Sattar, N., Cantin, B., Lamarche, B., Després, J. P., Null, E. Barrett Connor, Wingard, D. L., Daniels, L. B., Gudnason, V., Aspelund, T., Trevisan, M., Hofman, A., Franco, O. H., Tunstall Pedoe, H., Tavendale, R., Lowe, G. D. O., Woodward, M., Howard, W. J., Howard, B. V., Zhang, Y., Best, L. G., Umans, J., Davey Smith, G., Onat, A., Nakagawa, H., Sakurai, M., Nakamura, K., Morikawa, Y., Njølstad, I., Mathiesen, E. B., Wilsgaard, T., Sundström, J., Gaziano, J. M., Ridker, P. M., Marmot, M., Clarke, R., Collins, R., Fletcher, A., Brunner, E., Shipley, M., Kivimaki, M., Buring, J., Rifai, N., Cook, N., Ford, I., Robertson, M., Marín Ibañez, A., Feskens, E. J. M., Geleijnse, J. M., MUMC+: HVC Pieken Maastricht Studie (9), Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), RS: FHML non-thematic output, Apollo - University of Cambridge Repository, White, Ian R, Rapsomaniki, Eleni, and Panico, Salvatore

Subjects
Statistics and Probability, Male, Biometry, C-index, D-index, Discrimination, Analysis of Variance, Cardiovascular Diseases, Clinical Trials as Topic, Discriminant Analysis, Female, Humans, Middle Aged, Risk Factors, Survival Analysis, Medicine (all), Statistics, Probability and Uncertainty, 01 natural sciences, Article, Unit (housing), 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Survival data, Cardiovascular Disease, Covariate, Statistics, 030212 general & internal medicine, 0101 mathematics, 10. No inequality, Prognostic models, Survival analysis, Medicine(all), Proportional hazards model, Risk Factor, General Medicine, Probability and Uncertainty, Discriminant Analysi, Demography, Human
Abstract

Discrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell's C-index and Royston and Sauerbrei's D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data.To develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).We define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration.The proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices.The proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.? 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Published
2015
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12. Cadmium body burden and increased blood pressure in middle-aged American Indians: the Strong Heart Study

Author

Franceschini, N, primary, Fry, R C, additional, Balakrishnan, P, additional, Navas-Acien, A, additional, Oliver-Williams, C, additional, Howard, A G, additional, Cole, S A, additional, Haack, K, additional, Lange, E M, additional, Howard, B V, additional, Best, L G, additional, Francesconi, K A, additional, Goessler, W, additional, Umans, J G, additional, and Tellez-Plaza, M, additional

Published
2016
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13. Covariate-adjusted measures of discrimination for survival data

Author

White, Ian R., Rapsomaniki, Eleni, Wannamethee, S. G., Morris, R. W., Willeit, J., Willeit, P., Santer, P., Kiechl, S., Wald, N., Ebrahim, S., Lawlor, D. A., Gallacher, J., Yarnell, J. W G, Ben-Shlomo, Y., Casiglia, E., Tikhonoff, V., Sutherland, S. E., Nietert, P. J., Keil, J. E., Bachman, D. L., Psaty, B. M., Cushman, M., Nordestgaard, B. G., Tybjærg-Hansen, A., Frikke-Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Pilotto, L., Vanuzzo, D., Simons, L. A., Friedlander, Y., McCallum, J., Price, J. F., McLachlan, S., Taylor, J. O., Guralnik, J. M., Wallace, R. B., Kohout, F. J., Cornoni-Huntley, J. C., Blazer, D. G., Phillips, C. L., Wareham, N. J., Khaw, K. T., Brenner, H., Schöttker, B., Müller, H. T., Rothenbacher, D., Nissinen, A., Donfrancesco, C., Harald, K., Jousilahti, P. R., Vartiainen, E., Salomaa, V., D'Agostino, R. B., Wolf, P. A., Vasan, R. S., Daimon, M., Oizumi, T., Kayama, T., Kato, T., Chetrit, A., Dankner, R., Lubin, F., Welin, L., Svärdsudd, K., Eriksson, H., Lappas, G., Lissner, L., Mehlig, K., Björkelund, C., Nagel, D., Kiyohara, Y., Arima, H., Ninomiya, T., Hata, J., Rodriguez, B., Dekker, J. M., Nijpels, G., Stehouwer, C. D A, Iso, H., Kitamura, A., Yamagishi, K., Noda, H., Goldbourt, U., Kauhanen, J., Salonen, J. T., Tuomainen, T. P., Meade, T. W., DeStavola, B. L., Blokstra, A., Verschuren, W. M M, de Boer, I. H., Folsom, A. R., Koenig, W., Meisinger, C., Peters, A., Bueno-de-Mesquita, H. B., Rosengren, A., Wilhelmsen, L., Kuller, L. H., Grandits, G., Cooper, J. A., Bauer, K. A., Davidson, K. W., Kirkland, S., Shaffer, J. A., Shimbo, D., Sato, S., Dullaart, R. P F, Bakker, S. J L, Gansevoort, R. T., Ducimetiere, P., Amouyel, P., Arveiler, D., Evans, A., Ferrières, J., Schulte, H., Assmann, G., Jukema, J. W., Westendorp, R. G J, Sattar, N., Cantin, B., Lamarche, B., Després, J. P., E.Barrett-Connor, Wingard, D. L., Daniels, L. B., Gudnason, V., Aspelund, T., Trevisan, M., Hofman, A., Franco, O. H., Tunstall-Pedoe, H., Tavendale, R., Lowe, G. D O, Woodward, M., Howard, W. J., Howard, B. V., Zhang, Y., Best, L. G., Umans, J., Davey-Smith, G., Onat, A., Nakagawa, H., Sakurai, M., Nakamura, K., Morikawa, Y., Njølstad, I., Mathiesen, E. B., Wilsgaard, T., Sundström, J., Gaziano, J. M., Ridker, P. M., Marmot, M., Clarke, R., Collins, R., Fletcher, A., Brunner, E., Shipley, M., Kivimaki, M., Buring, J., Rifai, N., Cook, N., Ford, I., Robertson, M., Marín Ibañez, A., Feskens, E. J M, Geleijnse, J. M., White, Ian R., Rapsomaniki, Eleni, Wannamethee, S. G., Morris, R. W., Willeit, J., Willeit, P., Santer, P., Kiechl, S., Wald, N., Ebrahim, S., Lawlor, D. A., Gallacher, J., Yarnell, J. W G, Ben-Shlomo, Y., Casiglia, E., Tikhonoff, V., Sutherland, S. E., Nietert, P. J., Keil, J. E., Bachman, D. L., Psaty, B. M., Cushman, M., Nordestgaard, B. G., Tybjærg-Hansen, A., Frikke-Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Pilotto, L., Vanuzzo, D., Simons, L. A., Friedlander, Y., McCallum, J., Price, J. F., McLachlan, S., Taylor, J. O., Guralnik, J. M., Wallace, R. B., Kohout, F. J., Cornoni-Huntley, J. C., Blazer, D. G., Phillips, C. L., Wareham, N. J., Khaw, K. T., Brenner, H., Schöttker, B., Müller, H. T., Rothenbacher, D., Nissinen, A., Donfrancesco, C., Harald, K., Jousilahti, P. R., Vartiainen, E., Salomaa, V., D'Agostino, R. B., Wolf, P. A., Vasan, R. S., Daimon, M., Oizumi, T., Kayama, T., Kato, T., Chetrit, A., Dankner, R., Lubin, F., Welin, L., Svärdsudd, K., Eriksson, H., Lappas, G., Lissner, L., Mehlig, K., Björkelund, C., Nagel, D., Kiyohara, Y., Arima, H., Ninomiya, T., Hata, J., Rodriguez, B., Dekker, J. M., Nijpels, G., Stehouwer, C. D A, Iso, H., Kitamura, A., Yamagishi, K., Noda, H., Goldbourt, U., Kauhanen, J., Salonen, J. T., Tuomainen, T. P., Meade, T. W., DeStavola, B. L., Blokstra, A., Verschuren, W. M M, de Boer, I. H., Folsom, A. R., Koenig, W., Meisinger, C., Peters, A., Bueno-de-Mesquita, H. B., Rosengren, A., Wilhelmsen, L., Kuller, L. H., Grandits, G., Cooper, J. A., Bauer, K. A., Davidson, K. W., Kirkland, S., Shaffer, J. A., Shimbo, D., Sato, S., Dullaart, R. P F, Bakker, S. J L, Gansevoort, R. T., Ducimetiere, P., Amouyel, P., Arveiler, D., Evans, A., Ferrières, J., Schulte, H., Assmann, G., Jukema, J. W., Westendorp, R. G J, Sattar, N., Cantin, B., Lamarche, B., Després, J. P., E.Barrett-Connor, Wingard, D. L., Daniels, L. B., Gudnason, V., Aspelund, T., Trevisan, M., Hofman, A., Franco, O. H., Tunstall-Pedoe, H., Tavendale, R., Lowe, G. D O, Woodward, M., Howard, W. J., Howard, B. V., Zhang, Y., Best, L. G., Umans, J., Davey-Smith, G., Onat, A., Nakagawa, H., Sakurai, M., Nakamura, K., Morikawa, Y., Njølstad, I., Mathiesen, E. B., Wilsgaard, T., Sundström, J., Gaziano, J. M., Ridker, P. M., Marmot, M., Clarke, R., Collins, R., Fletcher, A., Brunner, E., Shipley, M., Kivimaki, M., Buring, J., Rifai, N., Cook, N., Ford, I., Robertson, M., Marín Ibañez, A., Feskens, E. J M, and Geleijnse, J. M.

Published
2015

14. Covariate-adjusted measures of discrimination for survival data

Author

AIOS Psychiatrie, Public Health Epidemiologie, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, MS MDL 1, Cancer, Affectieve & Psychotische Med., White, Ian R., Rapsomaniki, Eleni, Wannamethee, S. G., Morris, R. W., Willeit, J., Willeit, P., Santer, P., Kiechl, S., Wald, N., Ebrahim, S., Lawlor, D. A., Gallacher, J., Yarnell, J. W G, Ben-Shlomo, Y., Casiglia, E., Tikhonoff, V., Sutherland, S. E., Nietert, P. J., Keil, J. E., Bachman, D. L., Psaty, B. M., Cushman, M., Nordestgaard, B. G., Tybjærg-Hansen, A., Frikke-Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Pilotto, L., Vanuzzo, D., Simons, L. A., Friedlander, Y., McCallum, J., Price, J. F., McLachlan, S., Taylor, J. O., Guralnik, J. M., Wallace, R. B., Kohout, F. J., Cornoni-Huntley, J. C., Blazer, D. G., Phillips, C. L., Wareham, N. J., Khaw, K. T., Brenner, H., Schöttker, B., Müller, H. T., Rothenbacher, D., Nissinen, A., Donfrancesco, C., Harald, K., Jousilahti, P. R., Vartiainen, E., Salomaa, V., D'Agostino, R. B., Wolf, P. A., Vasan, R. S., Daimon, M., Oizumi, T., Kayama, T., Kato, T., Chetrit, A., Dankner, R., Lubin, F., Welin, L., Svärdsudd, K., Eriksson, H., Lappas, G., Lissner, L., Mehlig, K., Björkelund, C., Nagel, D., Kiyohara, Y., Arima, H., Ninomiya, T., Hata, J., Rodriguez, B., Dekker, J. M., Nijpels, G., Stehouwer, C. D A, Iso, H., Kitamura, A., Yamagishi, K., Noda, H., Goldbourt, U., Kauhanen, J., Salonen, J. T., Tuomainen, T. P., Meade, T. W., DeStavola, B. L., Blokstra, A., Verschuren, W. M M, de Boer, I. H., Folsom, A. R., Koenig, W., Meisinger, C., Peters, A., Bueno-de-Mesquita, H. B., Rosengren, A., Wilhelmsen, L., Kuller, L. H., Grandits, G., Cooper, J. A., Bauer, K. A., Davidson, K. W., Kirkland, S., Shaffer, J. A., Shimbo, D., Sato, S., Dullaart, R. P F, Bakker, S. J L, Gansevoort, R. T., Ducimetiere, P., Amouyel, P., Arveiler, D., Evans, A., Ferrières, J., Schulte, H., Assmann, G., Jukema, J. W., Westendorp, R. G J, Sattar, N., Cantin, B., Lamarche, B., Després, J. P., E.Barrett-Connor, Wingard, D. L., Daniels, L. B., Gudnason, V., Aspelund, T., Trevisan, M., Hofman, A., Franco, O. H., Tunstall-Pedoe, H., Tavendale, R., Lowe, G. D O, Woodward, M., Howard, W. J., Howard, B. V., Zhang, Y., Best, L. G., Umans, J., Davey-Smith, G., Onat, A., Nakagawa, H., Sakurai, M., Nakamura, K., Morikawa, Y., Njølstad, I., Mathiesen, E. B., Wilsgaard, T., Sundström, J., Gaziano, J. M., Ridker, P. M., Marmot, M., Clarke, R., Collins, R., Fletcher, A., Brunner, E., Shipley, M., Kivimaki, M., Buring, J., Rifai, N., Cook, N., Ford, I., Robertson, M., Marín Ibañez, A., Feskens, E. J M, Geleijnse, J. M., AIOS Psychiatrie, Public Health Epidemiologie, Circulatory Health, JC onderzoeksprogramma Cardiovasculaire Epidemiologie, MS MDL 1, Cancer, Affectieve & Psychotische Med., White, Ian R., Rapsomaniki, Eleni, Wannamethee, S. G., Morris, R. W., Willeit, J., Willeit, P., Santer, P., Kiechl, S., Wald, N., Ebrahim, S., Lawlor, D. A., Gallacher, J., Yarnell, J. W G, Ben-Shlomo, Y., Casiglia, E., Tikhonoff, V., Sutherland, S. E., Nietert, P. J., Keil, J. E., Bachman, D. L., Psaty, B. M., Cushman, M., Nordestgaard, B. G., Tybjærg-Hansen, A., Frikke-Schmidt, R., Giampaoli, S., Palmieri, L., Panico, S., Pilotto, L., Vanuzzo, D., Simons, L. A., Friedlander, Y., McCallum, J., Price, J. F., McLachlan, S., Taylor, J. O., Guralnik, J. M., Wallace, R. B., Kohout, F. J., Cornoni-Huntley, J. C., Blazer, D. G., Phillips, C. L., Wareham, N. J., Khaw, K. T., Brenner, H., Schöttker, B., Müller, H. T., Rothenbacher, D., Nissinen, A., Donfrancesco, C., Harald, K., Jousilahti, P. R., Vartiainen, E., Salomaa, V., D'Agostino, R. B., Wolf, P. A., Vasan, R. S., Daimon, M., Oizumi, T., Kayama, T., Kato, T., Chetrit, A., Dankner, R., Lubin, F., Welin, L., Svärdsudd, K., Eriksson, H., Lappas, G., Lissner, L., Mehlig, K., Björkelund, C., Nagel, D., Kiyohara, Y., Arima, H., Ninomiya, T., Hata, J., Rodriguez, B., Dekker, J. M., Nijpels, G., Stehouwer, C. D A, Iso, H., Kitamura, A., Yamagishi, K., Noda, H., Goldbourt, U., Kauhanen, J., Salonen, J. T., Tuomainen, T. P., Meade, T. W., DeStavola, B. L., Blokstra, A., Verschuren, W. M M, de Boer, I. H., Folsom, A. R., Koenig, W., Meisinger, C., Peters, A., Bueno-de-Mesquita, H. B., Rosengren, A., Wilhelmsen, L., Kuller, L. H., Grandits, G., Cooper, J. A., Bauer, K. A., Davidson, K. W., Kirkland, S., Shaffer, J. A., Shimbo, D., Sato, S., Dullaart, R. P F, Bakker, S. J L, Gansevoort, R. T., Ducimetiere, P., Amouyel, P., Arveiler, D., Evans, A., Ferrières, J., Schulte, H., Assmann, G., Jukema, J. W., Westendorp, R. G J, Sattar, N., Cantin, B., Lamarche, B., Després, J. P., E.Barrett-Connor, Wingard, D. L., Daniels, L. B., Gudnason, V., Aspelund, T., Trevisan, M., Hofman, A., Franco, O. H., Tunstall-Pedoe, H., Tavendale, R., Lowe, G. D O, Woodward, M., Howard, W. J., Howard, B. V., Zhang, Y., Best, L. G., Umans, J., Davey-Smith, G., Onat, A., Nakagawa, H., Sakurai, M., Nakamura, K., Morikawa, Y., Njølstad, I., Mathiesen, E. B., Wilsgaard, T., Sundström, J., Gaziano, J. M., Ridker, P. M., Marmot, M., Clarke, R., Collins, R., Fletcher, A., Brunner, E., Shipley, M., Kivimaki, M., Buring, J., Rifai, N., Cook, N., Ford, I., Robertson, M., Marín Ibañez, A., Feskens, E. J M, and Geleijnse, J. M.

Published
2015

15. Epidemiology and genetic determinants of progressive deterioration of glycaemia in American Indians: the Strong Heart Family Study

Author

Voruganti, V. S., Haack, K., Franceschini, N., Fabsitz, R. R., Lee, E. T., North, K. E., Almasy, L., MacCluer, J. W., Best, L. G., Göring, H. H. H., Meigs, J. B., Pankow, J. S., Cole, S. A., and Laston, S.

Abstract

Type 2 diabetes is a chronic, heterogeneous disease and a major risk factor for cardiovascular diseases. The underlying mechanisms leading to progression to type 2 diabetes are not fully understood and genetic tools may help to identify important pathways of glycaemic deterioration.

Published
2013
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16. Genome-wide linkage scan for plasma high density lipoprotein cholesterol, apolipoprotein A-1 and triglyceride variation among American Indian populations: the Strong Heart Family Study

Author

Howard, B V, Diego, V P, Lee, E T, Almasy, L, Goring, H H H, Best, L G, Shara, N M, Li, X, MacCluer, J W, Fabsitz, R R, North, K. E, Cole, S A, Monda, K L, Laston, S, and Haack, K

Subjects
lipids (amino acids, peptides, and proteins)
Abstract

Recent studies have identified chromosomal regions linked to variation in high density lipoprotein cholesterol (HDL-C), apolipoprotein A-1 (Apo A-1) and triglyceride (TG), although results have been inconsistent and previous studies of American Indian populations are limited

Published
2009
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17. Sex-specific interaction between APOE genotype and carbohydrate intake affects plasma HDL-C levels: the Strong Heart Family Study

Author

Howard, B. V., MacCluer, J. W., Mosher, M. J., North, K. E., Best, L. G., Lee, E. T., Lange, L. A., and Fabsitz, R. R.

Subjects
nutritional and metabolic diseases, lipids (amino acids, peptides, and proteins)
Abstract

Low plasma levels of high-density lipoprotein cholesterol (HDL-C) are identified as a risk factor for cardiovascular disease (CVD). Sexual dimorphism, however, is widely reported in both HDL-C and CVD, with the underlying explanations of these sexual differences not fully understood. HDL-C is a complex trait influenced by both genes and dietary factors. Here we examine evidence for a sex-specific effect of APOE and the macronutrient carbohydrate on HDL-C, triglycerides (TG) and apoprotein A-1 (ApoA-1) in a sample of 326 male and 423 female participants of the Strong Heart Family Study (SHFS). Using general estimating equations in SAS to account for kinship correlations, stratifying by sex, and adjusting for age, body mass index (BMI) and SHS center, we examine the relationship between APOE genotype and carbohydrate intake on circulating levels of HDL-C, TG, and ApoA-1 through a series of carbohydrate-by-sex interactions and stratified analyses. APOE-by-carbohydrate intake shows significant sex-specific effects. All males had similar decreases in HDL-C levels associated with increased carbohydrate intake. However, only those females with APOE-4 alleles showed significantly lower HDL-C levels as their percent of carbohydrate intake increased, while no association was noted between carbohydrate intake and HDL-C in those females without an APOE-4 allele. These findings demonstrate the importance of understanding sex differences in gene-by-nutrient interaction when examining the complex architecture of HDL-C variation.

Published
2008
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18. Linkage analysis of LDL cholesterol in American Indian populations: the Strong Heart Family Study

Author

Laston, S., Almasy, L., Fabsitz, R. R., Göring, H. H.H., Diego, V. P., Lee, E. T., Best, L. G., Cantu, T., MacCluer, J. W., Cole, S. A., North, K. E., and Howard, B. V.

Subjects
lipids (amino acids, peptides, and proteins)
Abstract

Previous studies have demonstrated that low density lipoprotein cholesterol (LDL-C) concentration is influenced by both genes and environment. Although rare genetic variants associated with Mendelian causes of increased LDL-C are known, only one common genetic variant has been identified, the apolipoprotein E gene (

Published
2006
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19. Bivariate genetic association of KIAA1797 with heart rate in American Indians: the Strong Heart Family Study

Author

Melton, P. E., primary, Rutherford, S., additional, Voruganti, V. S., additional, Goring, H. H. H., additional, Laston, S., additional, Haack, K., additional, Comuzzie, A. G., additional, Dyer, T. D., additional, Johnson, M. P., additional, Kent, J. W., additional, Curran, J. E., additional, Moses, E. K., additional, Blangero, J., additional, Barac, A., additional, Lee, E. T., additional, Best, L. G., additional, Fabsitz, R. R., additional, Devereux, R. B., additional, Okin, P. M., additional, Bella, J. N., additional, Broeckel, U., additional, Howard, B. V., additional, MacCluer, J. W., additional, Cole, S. A., additional, and Almasy, L., additional

Published
2010
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24. Linkage analysis of LDL cholesterol in American Indian populations: the Strong Heart Family Study

Author

North, K. E., Göring, H. H. H., Cole, S. A., Diego, V. P., Almasy, L., Laston, S., Cantu, T., Howard, B. V., Lee, E. T., Best, L. G., Fabsitz, R. R., and MacCluer, J. W.

Abstract

Previous studies have demonstrated that low density lipoprotein cholesterol (LDL-C) concentration is influenced by both genes and environment. Although rare genetic variants associated with Mendelian causes of increased LDL-C are known, only one common genetic variant has been identified, the apolipoprotein E gene (APOE). In an attempt to localize quantitative trait loci (QTLs) influencing LDL-C, we conducted a genome-wide linkage scan of LDL-C in participants of the Strong Heart Family Study (SHFS). Nine hundred eighty men and women, age 18 years or older, in 32 extended families at three centers (in Arizona, Oklahoma, and North and South Dakota) were phenotyped for LDL-C concentration and other risk factors. Using a variance component approach and the program SOLAR, and after accounting for the effects of covariates, we detected a QTL influencing LDL-C on chromosome 19, nearest marker D19S888 at 19q13.41 [logarithm of odds (LOD) = 4.3] in the sample from the Dakotas. This region on chromosome 19 includes many possible candidate genes, including the APOE/C1/C4/C2 gene cluster. In follow-up association analyses, no significant evidence for an association was detected with the APOE*ε2 and APOE*ε4 alleles (P = 0.76 and P = 0.53, respectively). Suggestive evidence of linkage to LDL-C was detected on chromosomes 3q, 4q, 7p, 9q, 10p, 14q, and 17q. These linkage signals overlap positive findings for lipid-related traits and harbor plausible candidate genes for LDL-C.

Published
2006

25. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Author

Hughes, Odessica, Bentley, Amy R., Breeze, Charles E., Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M., Gilliland, Thomas, Meyer, Mariah C., Du, Jiawen, Raffield, Laura M., Kramer, Holly, Morton, Robert W., Gouveia, Mateus H., Atkinson, Elizabeth G., Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D., Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G., Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O., Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C., Kooperberg, Charles, Krieger, Jose E., Lange, Ethan M., Lanktree, Matthew B., Lash, James P., Lotufo, Paulo A., Loos, Ruth J.F., Ha My, Vy Thi, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J., Rich, Stephen S., Rodriquez, Erik J., Tarazona-Santos, Eduardo, Taylor, Kent D., Umans, Jason G., Wen, Jia, Young, Bessie A., Yu, Zhi, Zhang, Ying, Ida Chen, Yii Der, Rundek, Tanja, Rotter, Jerome I., Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C., Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A., Carson, April P., Lange, Leslie A., Li, Yun, Perez-Stable, Eliseo J., Do, Ron, Charchar, Fadi J., Tomaszewski, Maciej, Mychaleckyj, Josyf C., Rotimi, Charles, Morris, Andrew P., Franceschini, Nora, Hughes, Odessica, Bentley, Amy R., Breeze, Charles E., Aguet, Francois, Xu, Xiaoguang, Nadkarni, Girish, Sun, Quan, Lin, Bridget M., Gilliland, Thomas, Meyer, Mariah C., Du, Jiawen, Raffield, Laura M., Kramer, Holly, Morton, Robert W., Gouveia, Mateus H., Atkinson, Elizabeth G., Valladares-Salgado, Adan, Wacher-Rodarte, Niels, Dueker, Nicole D., Guo, Xiuqing, Hai, Yang, Adeyemo, Adebowale, Best, Lyle G., Cai, Jianwen, Chen, Guanjie, Chong, Michael, Doumatey, Ayo, Eales, James, Goodarzi, Mark O., Ipp, Eli, Irvin, Marguerite Ryan, Jiang, Minzhi, Jones, Alana C., Kooperberg, Charles, Krieger, Jose E., Lange, Ethan M., Lanktree, Matthew B., Lash, James P., Lotufo, Paulo A., Loos, Ruth J.F., Ha My, Vy Thi, Peralta-Romero, Jesús, Qi, Lihong, Raffel, Leslie J., Rich, Stephen S., Rodriquez, Erik J., Tarazona-Santos, Eduardo, Taylor, Kent D., Umans, Jason G., Wen, Jia, Young, Bessie A., Yu, Zhi, Zhang, Ying, Ida Chen, Yii Der, Rundek, Tanja, Rotter, Jerome I., Cruz, Miguel, Fornage, Myriam, Lima-Costa, Maria Fernanda, Pereira, Alexandre C., Paré, Guillaume, Natarajan, Pradeep, Cole, Shelley A., Carson, April P., Lange, Leslie A., Li, Yun, Perez-Stable, Eliseo J., Do, Ron, Charchar, Fadi J., Tomaszewski, Maciej, Mychaleckyj, Josyf C., Rotimi, Charles, Morris, Andrew P., and Franceschini, Nora

Abstract

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10−8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.

Published
2024

26. Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction:An Individual-Participant-Data Meta-Analysis

Author

Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, Wood, Angela, Paige, Ellie, Barrett, Jessica, Pennells, Lisa, Sweeting, Michael, Willeit, Peter, Di Angelantonio, Emanuele, Gudnason, Vilmundur, Nordestgaard, Børge G, Psaty, Bruce M, Goldbourt, Uri, Best, Lyle G, Assmann, Gerd, Salonen, Jukka T, Nietert, Paul J, Verschuren, W M Monique, Brunner, Eric J, Kronmal, Richard A, Salomaa, Veikko, Bakker, Stephan J L, Dagenais, Gilles R, Sato, Shinichi, Jansson, Jan-Håkan, Willeit, Johann, Onat, Altan, de la Cámara, Agustin Gómez, Roussel, Ronan, Völzke, Henry, Dankner, Rachel, Tipping, Robert W, Meade, Tom W, Donfrancesco, Chiara, Kuller, Lewis H, Peters, Annette, Gallacher, John, Kromhout, Daan, Iso, Hiroyasu, Knuiman, Matthew, Casiglia, Edoardo, Kavousi, Maryam, Palmieri, Luigi, Sundström, Johan, Davis, Barry R, Njølstad, Inger, Couper, David, Danesh, John, Thompson, Simon G, and Wood, Angela

Abstract

The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.

Published
2017

27. Myocardial mechano-energetic efficiency and insulin resistance in non-diabetic members of the Strong Heart Study cohort

Author

Costantino Mancusi, Mary J. Roman, Elisa T. Lee, Wenyu Wang, Richard B. Devereux, Lyle G. Best, Ying Zhang, Barbara V. Howard, Giovanni de Simone, Mancusi, C., De Simone, G., Best, L. G., Wang, W., Zhang, Y., Roman, M. J., Lee, E. T., Howard, B. V., and Devereux, R. B.

Subjects
Adult, Male, medicine.medical_specialty, lcsh:Diseases of the circulatory (Cardiovascular) system, Adolescent, Endocrinology, Diabetes and Metabolism, Heart Ventricles, 030209 endocrinology & metabolism, Blood Pressure, 030204 cardiovascular system & hematology, Models, Biological, Ventricular Function, Left, Myocardial metabolism, 03 medical and health sciences, Ventricular Dysfunction, Left, Young Adult, 0302 clinical medicine, Insulin resistance, Oxygen Consumption, Heart Rate, Risk Factors, Diabetes mellitus, Internal medicine, medicine, Humans, Mass index, Aged, Original Investigation, Aged, 80 and over, medicine.diagnostic_test, business.industry, Myocardium, Cardiac function, Stroke volume, Middle Aged, medicine.disease, Blood pressure, Echocardiography, lcsh:RC666-701, Population study, Female, Metabolic syndrome, Insulin Resistance, Cardiology and Cardiovascular Medicine, Lipid profile, business, Energy Metabolism
Abstract

Background Myocardial energetic efficiency (MEE), is a strong predictor of CV events in hypertensive patient and is reduced in patients with diabetes and metabolic syndrome. We hypothesized that severity of insulin resistance (by HOMA-IR) negatively influences MEE in participants from the Strong Heart Study (SHS). Methods We selected non-diabetic participants (n = 3128, 47 ± 17 years, 1807 women, 1447 obese, 870 hypertensive) free of cardiovascular (CV) disease, by merging two cohorts (Strong Heart Study and Strong Heart Family Study, age range 18–93). MEE was estimated as stroke work (SW = systolic blood pressure [SBP] × stroke volume [SV])/“double product” of SBP × heart rate (HR), as an estimate of O2 consumption, which can be simplified as SV/HR ratio and expressed in ml/sec. Due to the strong correlation, MEE was normalized by left ventricular (LV) mass (MEEi). Results Linear trend analyses showed that with increasing quartiles of HOMA-IR patients were older, more likely to be women, obese and hypertensive, with a trend toward a worse lipid profile (all p for trend Conclusions Severity of insulin resistance has significant and independent negative impact on myocardial mechano-energetic efficiency in nondiabetic individual from a population study of American Indians. Trial registration number NCT00005134, Name of registry: Strong Heart Study, URL of registry: https://clinicaltrials.gov/ct2/show/NCT00005134, Date of registration: May 25, 2000, Date of enrolment of the first participant to the trial: September 1988

Published
2019
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28. Genetic and other factors determining mannose-binding lectin levels in American Indians: the Strong Heart Study

Author

Best, Lyle G, Ferrell, Robert E, Decroo, Susan, North, Kari E, Maccluer, Jean W, Zhang, Ying, Lee, Elisa T, Howard, Barbara V, Umans, Jason, Palmieri, Vittorio, Garred, Peter, Best, Lyle G, Ferrell, Robert E, Decroo, Susan, North, Kari E, Maccluer, Jean W, Zhang, Ying, Lee, Elisa T, Howard, Barbara V, Umans, Jason, Palmieri, Vittorio, and Garred, Peter

Abstract

Udgivelsesdato: 2009, BACKGROUND: Mannose-binding lectin (MBL) forms an integral part of the innate immune system. Persistent, subclinical infections and chronic inflammatory states are hypothesized to contribute to the pathogenesis of atherosclerosis. MBL gene (MBL2) variants with between 12 to 25% allele frequency in Caucasian and other populations, result in markedly reduced expression of functional protein. Prospective epidemiologic studies, including a nested, case-control study from the present population, have demonstrated the ability of MBL2 genotypes to predict complications of atherosclerosis,. The genetic control of MBL2 expression is complex and genetic background effects in specific populations are largely unknown. METHODS: The Strong Heart Study is a longitudinal, cohort study of cardiovascular disease among American Indians. A subset of individuals genotyped for the above mentioned case-control study were selected for analysis of circulating MBL levels by double sandwich ELISA method. Mean MBL levels were compared between genotypic groups and multivariate regression was used to determine other independent factors influencing MBL2 expression. RESULTS: Our results confirm the effects of variant structural (B, C, and D) and promoter (H and Y) alleles that have been seen in other populations. In addition, MBL levels were found to be positively associated with male gender and hemoglobin A1c levels, but inversely related to triglyceride levels. Correlation was not found between MBL and other markers of inflammation. CONCLUSION: New data is presented concerning the effects of known genetic variants on MBL levels in an American Indian population, as well as the relationship of MBL2 expression to clinical and environmental factors, including inflammatory markers.

Published
2009

29. Ανάλυση σφάλματος σχετικά με τη μέθοδο του Bergman χρησιμοποιώντας βάση που περιέχει αλγεβρικές συναρτήσεις

Author

Lytrides, Michael, Στυλιανόπουλος, Νικόλαος, Stylianopoulos, Nikolaos, Baratchart, Lourent, Khavinson, Dima, Vidras, Alekos, Koumandos, Stamatis, Βίδρας, Αλέκος, Κουμάντος, Σταμάτης, Πανεπιστήμιο Κύπρου, Σχολή Θετικών και Εφαρμοσμένων Επιστημών, Τμήμα Μαθηματικών και Στατιστικής, and University of Cyprus, Faculty of Pure and Applied Sciences, Department of Mathematics and Statistics

Subjects
ΘΕΩΡΗΤΙΚΗ ΑΙΤΙΟΛΟΓΗΣΗ ΤΩΝ ΔΙΑΦΟΡΩΝ ΣΦΑΛΜΑΤΩΝ ΤΗΣ ΜΕΘΟΔΟΥ BERGMAN, ΣΥΜΜΟΡΦΕΣ ΑΠΕΙΚΟΝΙΣΕΙΣ, ΒΑΣΗ ΠΟΥ ΠΕΡΙΕΧΕΙ ΑΛΓΕΒΡΙΚΕΣ ΣΥΝΑΡΤΗΣΕΙΣ, ΟΡΘΟΓΩΝΙΑ ΠΟΛΥΩΝΥΜΑ BIEBERBACH, BERGMAN KERNEL METHOD WITH AUGMENTED BASIS (BKM/AB), Mathematical analysis, BERGMAN KERNEL METHOD (BKM), SINGULAR BASIS FUNCTION, BERGMAN ORTHOGONAL POLYNOMIAL, Bergman kernel functions, BIEBERBACH POLYNOMIALS, ΑΝΑΛΥΣΗ ΣΦΑΛΜΑΤΟΣ ΣΧΕΤΙΚΑ ΜΕ ΤΗ ΜΕΘΟΔΟ BERGMAN, ΠΡΟΣΕΓΓΙΣΗ ΣΥΜΜΟΡΦΩΝ ΑΠΕΙΚΟΝΙΣΕΩΝ, Analytic functions, ΟΡΘΟΓΩΝΙΑ ΠΟΛΥΩΝΥΜΑ BERGMAN, NUMERICAL RESULT REGARDING THE APPLICATION OF THE BKM & BKM/AB, ΑΡΙΘΜΗΤΙΚΑ ΑΠΟΤΕΛΕΣΜΑΤΑ ΣΧΕΤΙΚΑ ΜΕ ΤΗ ΜΕΘΟΔΟ BERGMAN, ERROR ANALYSIS OF THE BKM, NUMERICAL CONFORMAL MAPPING, Numerical analysis
Abstract

Includes bibliography (p. 108-110). Number of sources in the bibliography: 27 University of Cyprus, Faculty of Pure and Applied Sciences, Department of Mathematics and Statistics, 2011 Στην παρούσα διατριβή υποθέτουμε ότι το G είναι ένα φραγμένο χωρίο Jordan με κατα τμήμα σύνορο μέσα στο μιγαδικό επίπεδο και έστω ότι Ω:= / συμβολίζει το συμπλήρωμα του . Παρουσιάζουμε θεωρητικές εκτιμήσεις και αριθμητικά αποτελέσματα σχετικά με την εφαρμογή της μεθόδου Bergman (ΒΚΜ) χρησιμοποιώντας μια βάση που περιέχει αλγεβρικές συναρτήσεις. Η μέθοδος αυτή είναι γνωστή ως ΒΚΜ/ΑΒ για την προσέγγιση της σύμμορφης απεικόνισης fo από το G στον κανονικοποιημένο δίσκο. Εδώ σημειώνουμε ότι παρουσιάζουμε δύο ειδών σφάλματα για την προσέγγισης της fo από τα βέλτιστα L (G) πολυώνυμα: το L (G) σφάλμα και το L ( ) σφάλμα. Με αυτό τον τρόπο παρέχουμε πλήρη θεωρητική αιτιολόγηση της μεθόδου ΒΚΜ/ΑΒ. Τέλος, παρουσιάζουμε αριθμητικά αποτελέσματα που πιστοποιούν όλα τα θεωρητικά μας αποτελέσματα για τη μέθοδο ΒΚΜ και ΒΚΜ/ΑΒ. Ιn this thesis we assume that G is a bounded Jordan domain in the complex plane with piecewise analytic boundary and let Ω: = / denotes the complement of . We present theoretical estimates and numerical evidence for certain phenomena, regarding the application of the Brgman Kernel method (BKM) with algebraic and pole singular basis functions denoted as BKM/AB, for approximating the conformal mapping fo of G onto the normalized disk. More precisely, we obtain two sided – estimates for the L (G) and L ( ) – error, in the best L (G) polynomial approximation to fo. In this way, we complete the task of providing full theoretical justification of this method. Finally, we present numerical computations that illustrate our theoretical results for the method BKM and BKM/AB.

Published
2011

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