Annual mid-year population estimates for those aged 90 years and over by sex and single year of age (90 to 104 years), and the 105 years and over age group, UK.

This dataset provides Census 2021 estimates that classify usual residents aged 100 years and over living with others in a private household in England and Wales by relationship. The estimates are as at Census Day, 21 March 2021.

There were an estimated 960 centenarians (people aged 100 and over) in Scotland in mid-2020. This is a 13% increase over mid-2019, and is the highest ever figure. This reflects the increase of births which occurred in Scotland at the end of World War One. The majority of centenarians are female. In mid-2020, there were 4 times as many female centenarians as males. There were 770 females compared to 190 males. This disparity can be explained by the difference in life expectancy for males and females. Over the past decade to mid-2020, the number of male centenarians has grown at a faster rate than female centenarians. For the 90 and over age group, numbers of males have increased by almost 70% since mid-2010, while the females have grown by just under 24%. In mid-2020, there were an estimated 43,750 people aged 90 and over in Scotland. The number of people in this age group has increased every year since mid-2010 when there were 32,440 people aged 90 and over.

Chinese Longitudinal Healthy Longevity Survey (CLHLS) WELCOME! The Chinese Longitudinal Healthy Longevity Survey (CLHLS) has been supported by NIA/NIH grants R01 AG023627-01 (PI: Zeng Yi) (Grant name: Demographic Analysis of Healthy Longevity in China) and P01 AG 008761 (PI: Zeng Yi; Program Project Director: James W. Vaupel), awarded to Duke University, with Chinese matching support for personnel costs and some local expenses. UNFPA and the China Social Sciences Foundation provided additional support for expanding the 2002 CLHLS survey. The Max Planck Institute for Demographic Research has provided support for international training since the CLHLS 1998 baseline survey. Finally, in December 2004 the China Natural Sciences Foundation and the Hong Kong Research Grants Council (RGC) partnered with NIA/NIH, providing grants to partially support the CLHLS project. Until present, the CLHLS conducted face-to-face interviews with 8,959, 11,161, 20,421, 18,524 and 19,863 individuals in 1998, 2000, 20002, 2005, and 2008-09, respectively, using internationally compatible questionnaires. Among the approximately 80,000 interviews conducted in the five waves, 14,290 were with centenarians, 18,910 with nonagenarians, 20,743 with octogenarians, 14,416 with younger elders aged 65-79, and 10,569 with middle-age adults aged 35-64. At each wave, survivors were re-interviewed, and deceased interviewees were replaced with new participants. Data on mortality and health status before dying for the 17,721 elders aged 65-110 who died between waves were collected in interviews with a close family member of the deceased. The CLHLS has the largest sample of centenarians in the world according to a report in Science (see the report). Our general goal is to shed new light on a better understanding of the determinants of healthy longevity of human beings. We are compiling extensive data on a much larger population of the oldest-old aged 80-112 than has previously been studied, with a comparison group of younger elders aged 65-79. We propose to use innovative demographic and statistical methods to analyze longitudinal survey data. Our goal is to determine which factors, out of a large set of social, behavioral, biological, and environmental risk factors, play an important role in healthy longevity. The large population size, the focus on healthy longevity (rather than on a specific disease or disorder), the simultaneous consideration of various risk factors, and the use of analytical strategies based on demographic concepts make this an innovative demographic data collection and research project. Our specific objectives are as follows: Collect intensive individual interview data including health, disability, demographic, family, socioeconomic, and behavioral risk factors for mortality and healthy longevity. Follow up the oldest-old and the comparison group of the younger elders, as well as some of the elders’ adult children to ascertain changes in their health status, care needs and costs, and associated factors. We will also ascertain mortality and causes of death, as well as care needs, costs, and health/disability status before death. Analyze the collected data to estimate the impacts of social, behavioral, environmental, and biological risk factors that are determinants of healthy longevity and mortality in the oldest-old. Compare the findings with results from other studies of large populations at advanced age.

There is a growing interest in studying the microbiota associated with aging by integrating multiple longevity researches while minimizing the influence of confounding factors. Here, we reprocessed metagenomic sequencing data from four different aging research studies and evaluated potential confounding factors in order to minimize the batch effect. Subsequently, we detected the diversity and abundance of the gut microbiome in three different age cohorts. Out of 1053 different bacteria species, only four showed substantial depletion across different age groups: Ligilactobacillus ruminis, Turicibacter sp. H121, Blautia massiliensis, and Anaerostipes hadrus. Archaea accumulated more in young individuals compared to elderly and centenarians. Candida albicans was more prevalent in centenarians, but Nakaseomyces glabratus (also known as Candida glabrata) was more common in elderly adults. Shuimuvirus IME207 showed a significant increase in centenarians compared to both control groups. In addition, we utilized a Fisher’s exact test to investigate topological properties of differentially abundant microbiota in the co-occurrence network of each age group. Microbial signatures specific to different age stages were identified based on the condition: the reads showing differential abundance were higher compared to the other age groups. Lastly, we selected Methanosarcina sp. Kolksee for the Y group, Prevotella copri for the E group and Shuimuvirus IME207 for the C group as representatives of age-related characteristics to study how their interactions change during the aging process. Our results provide crucial insights into the gut microbiome’s ecological dynamics in relation to the aging process.

APrevalence values of subsets of group F, FC (offspring who lived with their centenarian parents) and FN (offspring who do not live with their centenarian parents) are presented in between brackets.